diff --git a/dev/swebench/README_art_style.md b/dev/swebench/README_art_style.md
new file mode 100644
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--- /dev/null
+++ b/dev/swebench/README_art_style.md
@@ -0,0 +1,142 @@
+# ART-Style Training for SWE-bench
+
+This implementation provides an ART (Agent Reinforcement Trainer) style training script for SWE-bench, inspired by `qwen_rollout.py` but following idiomatic ART patterns.
+
+## Files
+
+- `art_style_rollout.py` - Core rollout function that executes agent interactions in ART style
+- `train_art_style.py` - Main training script with both inference and training modes
+- `test_art_style.py` - Test script to verify the implementation
+
+## Key Features
+
+### ART-Style Rollout (`art_style_rollout.py`)
+
+The rollout function follows ART idioms:
+- Returns `art.Trajectory` objects with messages, rewards, and metrics
+- Uses retry decorators for robustness
+- Tracks detailed metrics including progress, maintenance, and resolution
+- Implements proper tool handling for bash commands and file editing
+- Calculates rewards based on test pass/fail rates
+
+### Training Script (`train_art_style.py`)
+
+Supports two modes:
+
+1. **Inference Mode** - For testing with existing models:
+   ```bash
+   python train_art_style.py --mode inference --num-instances 10
+   ```
+
+2. **Training Mode** - For training new models with gradients:
+   ```bash
+   python train_art_style.py --mode train --epochs 1 --batch-size 4
+   ```
+
+### Configuration
+
+The `ARTModelConfig` class allows customization of:
+- `max_steps`: Maximum interaction steps (default: 30)
+- `temperature`: Model temperature (default: 0.0)
+- `max_tokens`: Maximum tokens per response (default: 4096)
+- `system_prompt`: System prompt for the model
+- `instance_prompt_template`: Template for problem descriptions
+
+### Command-Line Options
+
+Key training script options:
+- `--mode`: Choose between `inference` (no gradients) or `train` (with gradients)
+- `--model`: Model name or path
+- `--num-instances`: Number of instances to use (inference mode)
+- `--batch-size`: Batch size for training
+- `--rollouts-per-instance`: Number of rollouts per instance
+- `--epochs`: Number of training epochs
+- `--learning-rate`: Learning rate for training
+- `--reward-power`: Power to apply to progress metric
+- `--no-quality-filter`: Disable quality filtering (not recommended)
+- `--require-non-zero-tests`: Require instances to have tests (default: True)
+
+## Usage Examples
+
+### Quick Test
+```bash
+# Test a single instance
+python test_art_style.py
+
+# Test a trajectory group
+python test_art_style.py group
+```
+
+### Inference with Local Model
+```bash
+python train_art_style.py \
+    --mode inference \
+    --model "willcb/Qwen3-32B" \
+    --api-base "http://localhost:8000/v1" \
+    --num-instances 5 \
+    --rollouts-per-instance 2
+```
+
+### Disable Quality Filtering (Not Recommended)
+```bash
+# Use all instances without quality filtering
+python train_art_style.py \
+    --mode inference \
+    --model "willcb/Qwen3-32B" \
+    --num-instances 10 \
+    --no-quality-filter
+```
+
+### Training a New Model
+```bash
+python train_art_style.py \
+    --mode train \
+    --model "Qwen/Qwen3-32B" \
+    --batch-size 4 \
+    --rollouts-per-instance 4 \
+    --epochs 1 \
+    --learning-rate 5e-5
+```
+
+## Reward Calculation
+
+The reward function follows the same formula as the original implementation:
+- 20% weight on test maintenance (keeping passing tests passing)
+- 30% weight on progress (fixing failing tests)
+- 50% weight on full resolution (all tests passing)
+
+The `reward_power` parameter can be used to adjust the progress component.
+
+## Quality Filtering
+
+The implementation includes an instance quality filter that identifies instances with reliable test behavior. **Quality filtering is enabled by default.**
+
+- **Filters instances where**:
+  1. All FAIL_TO_PASS tests initially pass
+  2. All FAIL_TO_PASS tests fail after applying the patch (bug introduction)
+  3. All PASS_TO_PASS tests remain passing
+
+- **Usage**: Quality filtering is automatic. To disable it (not recommended), use `--no-quality-filter`
+- **Statistics**: Approximately 54% of instances (4,577 out of 8,480) meet quality criteria
+
+## Differences from Original Implementation
+
+1. **Simplified Architecture**: No dependency on SWE-Agent framework
+2. **Direct Tool Implementation**: Tools are implemented directly without complex abstractions
+3. **ART Integration**: Native support for ART training loops and trajectory management
+4. **Cleaner Error Handling**: Uses ART retry decorators and proper exception handling
+5. **Quality Filtering**: Built-in filtering based on test reliability
+
+## Requirements
+
+- Python 3.8+
+- ART framework (`art`)
+- OpenAI Python client
+- PyTorch (for training mode)
+- Access to SWE-bench instances and sandboxes
+
+## Environment Variables
+
+- `OPENAI_API_KEY`: API key for OpenAI (can be "default" for local inference)
+- `OPENAI_BASE_URL`: Base URL for API (e.g., "http://localhost:8000/v1")
+- Standard SWE-bench environment variables
\ No newline at end of file
diff --git a/dev/swebench/analysis/INVESTIGATION_NOTES.md b/dev/swebench/analysis/INVESTIGATION_NOTES.md
new file mode 100644
index 00000000..77a5e95b
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+++ b/dev/swebench/analysis/INVESTIGATION_NOTES.md
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+# SWE-bench Investigation Notes
+
+**Last Updated**: 2025-07-10  
+**Analysis Summary**: 74.6% perfect pass rate (6,329/8,480 instances)
+
+## Critical Issues: Repositories with 0% Perfect Pass Rate
+
+### 1. P2P-Dominant Failures (Patches Break Existing Tests)
+
+These repositories have near-100% P2P failure rates, meaning patches consistently break existing functionality:
+
+#### **oauthlib** (164/166 instances fail P2P - 98.8%)
+- **Pattern**: All patches break existing OAuth functionality tests
+- **Example instances**:
+  - `oauthlib__oauthlib.1fd52536.lm_rewrite__q9ve64pd`: 670 P2P failures
+  - `oauthlib__oauthlib.1fd52536.lm_rewrite__rchxmbd6`: 625 P2P failures
+- **Hypothesis**: OAuth library may have tightly coupled components where any change breaks auth flows
+
+#### **cloudpipe** (37/37 instances fail P2P - 100%)
+- **Pattern**: Every single patch breaks existing tests
+- **Hypothesis**: Likely has integration tests that are sensitive to any code changes
+
+#### **pyparsing** (28/29 instances fail P2P - 96.6%)
+- **Pattern**: Parser modifications break existing parsing tests
+- **Hypothesis**: Grammar/parser changes have cascading effects
+
+### 2. F2P-Initial Failures (Baseline Tests Don't Pass)
+
+These repositories have tests that fail even before applying patches:
+
+#### **Project-MONAI** (159/161 instances fail F2P-initial - 98.8%)
+- **Pattern**: Tests don't pass in baseline state
+- **Hypothesis**: May require specific GPU/CUDA setup or have incorrect test specifications
+
+#### **burnash** (26/26 instances fail F2P-initial - 100%)
+- **Pattern**: All baseline tests fail
+- **Hypothesis**: Test specifications may be incorrect or environment setup issues
+
+#### **python-trio** (90/139 instances fail F2P-initial - 64.7%)
+- **Pattern**: Mix of F2P-initial (90) and P2P (125) failures
+- **Example**: `python-trio__trio.cfbbe2c1.pr_2937`: 730 F2P initial failures
+- **Hypothesis**: Async testing framework may have special requirements
+
+### 3. Mixed Failure Patterns
+
+#### **seperman** (170 P2P + 115 F2P-initial failures out of 172 instances)
+- **Pattern**: Both baseline and regression failures
+- **Hypothesis**: Fundamental test environment or specification issues
+
+#### **django-money**, **facebookresearch**, **tweepy**, **aio-libs**
+- All show similar patterns of both F2P-initial and P2P failures
+- Suggests both incorrect test specs AND patches that break functionality
+
+## Root Cause Analysis
+
+### Environmental Issues
+1. **GPU/CUDA Requirements**: Project-MONAI likely needs GPU setup
+2. **Async Test Runners**: python-trio, aio-libs may need special async test configurations
+3. **Database/Services**: django-money might need database setup
+
+### Test Specification Issues
+1. **Incorrect Baseline**: F2P tests that fail initially suggest wrong test selections
+2. **Version Mismatches**: Tests may be from different versions than the code
+
+### Patch Quality Issues
+1. **Over-broad Changes**: Patches might modify more than intended
+2. **Missing Context**: Patches may not account for all usages of modified code
+
+## Debugging Strategy
+
+### Quick Checks
+```bash
+# Check a specific failing instance
+uv run python analyze_results.py | grep "oauthlib__oauthlib"
+
+# Look at error patterns for a specific repo
+grep "oauthlib" unique_errors.txt
+
+# Run a single instance manually
+async with new_sandbox(image="swesmith/oauthlib__oauthlib.1fd52536", provider="daytona") as sandbox:
+    failed, passed = await sandbox.eval(instance["FAIL_TO_PASS"])
+```
+
+### Deep Investigation Steps
+1. **Pick one instance from each failure category**
+2. **Run with verbose logging to see actual test output**
+3. **Compare working vs failing repos to identify patterns**
+4. **Check if special test commands or setup is needed**
+
+## Recommendations for Future Work
+
+### High Priority
+1. **oauthlib**: Investigate why ALL patches break OAuth flows
+2. **Project-MONAI**: Check GPU requirements and test specifications
+3. **seperman**: High volume of failures (170) makes this impactful to fix
+
+### Medium Priority
+1. **python-trio**: Mixed failures suggest complex issues
+2. **cloudpipe**: Small repo but 100% failure rate is concerning
+
+### System Improvements
+1. Add pre-flight checks for GPU/special requirements
+2. Validate test specifications before running
+3. Add regression test validation to patch generation
+4. Consider repo-specific configuration overrides
+
+## Success Metrics
+- Current: 74.6% perfect pass rate
+- Goal: >85% by addressing top 5 problematic repos
+- 52 repos already at 100% - system works well for standard cases
\ No newline at end of file
diff --git a/dev/swebench/analysis/SWE_BENCH_ANALYSIS_REPORT.md b/dev/swebench/analysis/SWE_BENCH_ANALYSIS_REPORT.md
new file mode 100644
index 00000000..e0299756
--- /dev/null
+++ b/dev/swebench/analysis/SWE_BENCH_ANALYSIS_REPORT.md
@@ -0,0 +1,103 @@
+# SWE-Bench Test Results Analysis Report
+
+**Date**: 2025-07-10  
+**Total Instances Analyzed**: 8,480 (unique instances, latest run per instance)
+
+## Executive Summary
+
+### Overall Performance
+- **Perfect Pass Rate**: 74.6% (6,329/8,480)
+- **Test Failures**: 24.2% (2,055 instances)
+- **Errors**: 1.5% (122 instances)
+
+A "perfect pass" means:
+- No errors during sandbox creation or test execution
+- All FAIL_TO_PASS tests passed initially (baseline behavior)
+- All FAIL_TO_PASS tests failed after patch (patch introduces intended bug)
+- All PASS_TO_PASS tests passed (no regression)
+
+## Key Findings
+
+### 1. Most Common Failure Modes
+
+| Failure Type | Count | Percentage | Description |
+|-------------|-------|------------|-------------|
+| P2P Failures | 1,758 | 20.7% | Patches broke existing functionality |
+| F2P Initial Failures | 1,171 | 13.8% | Tests failed baseline (should pass initially) |
+| F2P Post Pass | 182 | 2.1% | Tests still passed after patch (should fail) |
+| Execution Errors | 122 | 1.5% | Timeouts, sandbox failures, etc. |
+
+### 2. Repositories with Fundamental Issues (0% Perfect Pass Rate)
+
+15 repositories achieved 0% perfect pass rate, indicating systematic problems:
+
+#### P2P-Dominant Failures (patches consistently break existing tests):
+- **oauthlib**: 164/166 instances fail P2P (98.8%)
+- **cloudpipe**: 37/37 instances fail P2P (100%)
+- **pyparsing**: 28/29 instances fail P2P (96.6%)
+- **life4**: 40/43 instances fail P2P (93.0%)
+- **borntyping**: 16/17 instances fail P2P (94.1%)
+
+#### F2P-Initial Dominant Failures (baseline tests don't pass):
+- **Project-MONAI**: 159/161 instances fail F2P-initial (98.8%)
+- **burnash**: 26/26 instances fail F2P-initial (100%)
+- **python-trio**: 90/139 instances fail F2P-initial (64.7%)
+- **Cog-Creators**: 64/91 instances fail F2P-initial (70.3%)
+- **alanjds**: 18/27 instances fail F2P-initial (66.7%)
+
+#### Mixed Failures (both baseline and regression issues):
+- **seperman**: 115 F2P-initial + 170 P2P failures (172 instances)
+- **django-money**: 67 F2P-initial + 67 P2P failures (68 instances)
+- **facebookresearch**: 57 F2P-initial + 71 P2P failures (72 instances)
+- **tweepy**: 52 F2P-initial + 52 P2P failures (52 instances)
+- **aio-libs**: 8 F2P-initial + 8 P2P failures (8 instances)
+
+### 3. Repository Performance Distribution
+
+| Perfect Pass Rate | Repository Count | Percentage |
+|------------------|------------------|------------|
+| 0% | 15 | 14.3% |
+| 1-25% | 5 | 4.8% |
+| 26-50% | 4 | 3.8% |
+| 51-75% | 3 | 2.9% |
+| 76-99% | 26 | 24.8% |
+| 100% | 52 | 49.5% |
+
+### 4. Error Analysis
+
+Most errors (1.5% of instances) were infrastructure-related:
+- 504 Gateway Timeout: 76 instances
+- Command execution timeout: 64 instances
+- Empty command errors: 31 instances
+- Sandbox creation failures: 10 instances
+
+## Recommendations
+
+### High Priority Investigations
+
+1. **oauthlib & cloudpipe**: Near 100% P2P failure rate suggests patches consistently break core functionality. These need immediate review of patch generation logic.
+
+2. **Project-MONAI & burnash**: 100% F2P-initial failure rate indicates test specifications may be incorrect or tests are not properly capturing baseline behavior.
+
+3. **seperman**: Highest absolute number of failures (170 P2P failures) combined with F2P-initial failures suggests both test specification and patch generation issues.
+
+### System Improvements
+
+1. **Test Specification Review**: 13.8% F2P-initial failure rate suggests many test specifications don't correctly capture baseline behavior.
+
+2. **Patch Quality**: 20.7% P2P failure rate indicates patches often break existing functionality. Consider adding regression checks.
+
+3. **Infrastructure**: The 8,000 test limit resolved previous issues with test count limits.
+
+## Success Stories
+
+- 52 repositories (49.5%) achieved 100% perfect pass rate
+- 74.6% overall perfect pass rate shows the system works well for most cases
+- Very low error rate (1.5%) indicates stable infrastructure
+
+## Technical Notes
+
+- Analysis based on latest run per instance (aggregated by instance_id)
+- Used daytona provider with up to 128 concurrent sandboxes
+- Retry logic with exponential backoff for transient failures
+- Test count limit increased from 3,000 to 8,000
\ No newline at end of file
diff --git a/dev/swebench/analysis/analyze_repo_perfect_rates.py b/dev/swebench/analysis/analyze_repo_perfect_rates.py
new file mode 100644
index 00000000..2d1b2a94
--- /dev/null
+++ b/dev/swebench/analysis/analyze_repo_perfect_rates.py
@@ -0,0 +1,266 @@
+#!/usr/bin/env python3
+"""
+Analyze perfect pass rates by repository to identify those with fundamental issues.
+"""
+
+import json
+
+import polars as pl
+
+
+def load_and_analyze_by_repo():
+    """Load test results and analyze perfect pass rates by repository."""
+
+    # Read JSONL file and flatten the nested structure
+    flattened_data = []
+    with open("instance_test_results.jsonl", "r") as f:
+        for i, line in enumerate(f):
+            record = json.loads(line)
+            flattened = {
+                "instance_id": record.get("instance_id"),
+                "repo": record.get("repo"),
+                "error": record.get("error"),
+                "f2p_initial_failed": record.get("f2p_initial", {}).get("failed"),
+                "f2p_initial_passed": record.get("f2p_initial", {}).get("passed"),
+                "f2p_initial_error": record.get("f2p_initial", {}).get("error"),
+                "f2p_post_failed": record.get("f2p_post_patch", {}).get("failed"),
+                "f2p_post_passed": record.get("f2p_post_patch", {}).get("passed"),
+                "f2p_post_error": record.get("f2p_post_patch", {}).get("error"),
+                "p2p_failed": record.get("p2p", {}).get("failed"),
+                "p2p_passed": record.get("p2p", {}).get("passed"),
+                "p2p_error": record.get("p2p", {}).get("error"),
+                "_line_number": i,
+            }
+            flattened_data.append(flattened)
+
+    # Create DataFrame
+    schema = {
+        "instance_id": pl.Utf8,
+        "repo": pl.Utf8,
+        "error": pl.Utf8,
+        "f2p_initial_failed": pl.Int64,
+        "f2p_initial_passed": pl.Int64,
+        "f2p_initial_error": pl.Utf8,
+        "f2p_post_failed": pl.Int64,
+        "f2p_post_passed": pl.Int64,
+        "f2p_post_error": pl.Utf8,
+        "p2p_failed": pl.Int64,
+        "p2p_passed": pl.Int64,
+        "p2p_error": pl.Utf8,
+        "_line_number": pl.Int64,
+    }
+
+    df = pl.DataFrame(flattened_data, schema=schema)
+
+    # Take the last occurrence of each instance_id
+    df = df.sort("_line_number").group_by("instance_id").last().drop("_line_number")
+
+    # Extract repository name from instance_id
+    df = df.with_columns(
+        pl.col("instance_id").str.extract(r"^([^_]+)", 1).alias("repo_name")
+    )
+
+    # Define perfect pass criteria
+    perfect_mask = (
+        (pl.col("error").is_null())
+        & (pl.col("f2p_initial_error").is_null())
+        & (pl.col("f2p_post_error").is_null())
+        & (pl.col("p2p_error").is_null())
+        & (pl.col("f2p_initial_failed") == 0)  # All F2P should pass initially
+        & (pl.col("f2p_post_passed") == 0)  # All F2P should fail after patch
+        & (pl.col("p2p_failed") == 0)  # All P2P should pass
+    )
+
+    # Calculate stats by repository
+    repo_stats = []
+    for repo_name in df.select("repo_name").unique().to_series().to_list():
+        if repo_name:
+            repo_df = df.filter(pl.col("repo_name") == repo_name)
+            total = len(repo_df)
+            perfect = len(repo_df.filter(perfect_mask))
+
+            # Count different failure types
+            with_errors = len(repo_df.filter(pl.col("error").is_not_null()))
+            f2p_initial_failures = len(
+                repo_df.filter(
+                    (pl.col("error").is_null()) & (pl.col("f2p_initial_failed") > 0)
+                )
+            )
+            f2p_post_failures = len(
+                repo_df.filter(
+                    (pl.col("error").is_null()) & (pl.col("f2p_post_passed") > 0)
+                )
+            )
+            p2p_failures = len(
+                repo_df.filter((pl.col("error").is_null()) & (pl.col("p2p_failed") > 0))
+            )
+
+            repo_stats.append(
+                {
+                    "repo": repo_name,
+                    "total": total,
+                    "perfect": perfect,
+                    "perfect_rate": perfect / total * 100 if total > 0 else 0,
+                    "error_count": with_errors,
+                    "error_rate": with_errors / total * 100 if total > 0 else 0,
+                    "f2p_initial_fail_count": f2p_initial_failures,
+                    "f2p_initial_fail_rate": (
+                        f2p_initial_failures / total * 100 if total > 0 else 0
+                    ),
+                    "f2p_post_fail_count": f2p_post_failures,
+                    "f2p_post_fail_rate": (
+                        f2p_post_failures / total * 100 if total > 0 else 0
+                    ),
+                    "p2p_fail_count": p2p_failures,
+                    "p2p_fail_rate": p2p_failures / total * 100 if total > 0 else 0,
+                }
+            )
+
+    # Sort by perfect pass rate (ascending - worst first)
+    repo_stats.sort(key=lambda x: x["perfect_rate"])
+
+    return repo_stats
+
+
+def print_analysis(repo_stats):
+    """Print detailed analysis of repository perfect pass rates."""
+
+    print("=" * 120)
+    print("REPOSITORY PERFECT PASS RATE ANALYSIS")
+    print("=" * 120)
+    print(f"\nTotal repositories analyzed: {len(repo_stats)}")
+
+    # Overall stats
+    total_instances = sum(r["total"] for r in repo_stats)
+    total_perfect = sum(r["perfect"] for r in repo_stats)
+    print(
+        f"Overall perfect pass rate: {total_perfect}/{total_instances} ({total_perfect / total_instances * 100:.1f}%)"
+    )
+
+    # Repos with 0% perfect pass rate
+    zero_perfect = [r for r in repo_stats if r["perfect_rate"] == 0]
+    print(f"\nRepositories with 0% perfect pass rate: {len(zero_perfect)}")
+
+    print("\n" + "=" * 120)
+    print("BOTTOM 30 REPOSITORIES BY PERFECT PASS RATE")
+    print("=" * 120)
+    print(
+        f"{'Repository':<25} {'Total':>6} {'Perfect':>7} {'Rate':>6} | {'Errors':>6} {'F2P-I':>6} {'F2P-P':>6} {'P2P':>6}"
+    )
+    print("-" * 120)
+
+    for repo in repo_stats[:30]:
+        print(
+            f"{repo['repo']:<25} {repo['total']:>6} {repo['perfect']:>7} {repo['perfect_rate']:>5.1f}% | "
+            f"{repo['error_count']:>6} {repo['f2p_initial_fail_count']:>6} "
+            f"{repo['f2p_post_fail_count']:>6} {repo['p2p_fail_count']:>6}"
+        )
+
+    # Analyze failure patterns for worst performers
+    print("\n" + "=" * 120)
+    print("DETAILED ANALYSIS OF WORST PERFORMERS (0% PERFECT PASS RATE)")
+    print("=" * 120)
+
+    # Group by primary failure mode
+    primary_failures = {
+        "errors": [],
+        "f2p_initial": [],
+        "f2p_post": [],
+        "p2p": [],
+        "mixed": [],
+    }
+
+    for repo in zero_perfect:
+        if repo["error_rate"] > 50:
+            primary_failures["errors"].append(repo)
+        elif repo["f2p_initial_fail_rate"] > 50:
+            primary_failures["f2p_initial"].append(repo)
+        elif repo["f2p_post_fail_rate"] > 50:
+            primary_failures["f2p_post"].append(repo)
+        elif repo["p2p_fail_rate"] > 50:
+            primary_failures["p2p"].append(repo)
+        else:
+            primary_failures["mixed"].append(repo)
+
+    print(f"\nPrimarily Error Issues: {len(primary_failures['errors'])} repos")
+    for repo in primary_failures["errors"][:5]:
+        print(
+            f"  - {repo['repo']}: {repo['error_rate']:.1f}% error rate ({repo['error_count']}/{repo['total']})"
+        )
+
+    print(
+        f"\nPrimarily F2P Initial Failures: {len(primary_failures['f2p_initial'])} repos"
+    )
+    for repo in primary_failures["f2p_initial"][:5]:
+        print(
+            f"  - {repo['repo']}: {repo['f2p_initial_fail_rate']:.1f}% F2P-initial fail rate ({repo['f2p_initial_fail_count']}/{repo['total']})"
+        )
+
+    print(f"\nPrimarily F2P Post Failures: {len(primary_failures['f2p_post'])} repos")
+    for repo in primary_failures["f2p_post"][:5]:
+        print(
+            f"  - {repo['repo']}: {repo['f2p_post_fail_rate']:.1f}% F2P-post fail rate ({repo['f2p_post_fail_count']}/{repo['total']})"
+        )
+
+    print(f"\nPrimarily P2P Failures: {len(primary_failures['p2p'])} repos")
+    for repo in sorted(primary_failures["p2p"], key=lambda x: x["total"], reverse=True)[
+        :10
+    ]:
+        print(
+            f"  - {repo['repo']}: {repo['p2p_fail_rate']:.1f}% P2P fail rate ({repo['p2p_fail_count']}/{repo['total']})"
+        )
+
+    print(f"\nMixed Failure Patterns: {len(primary_failures['mixed'])} repos")
+    for repo in primary_failures["mixed"][:5]:
+        print(
+            f"  - {repo['repo']}: E={repo['error_rate']:.0f}%, F2P-I={repo['f2p_initial_fail_rate']:.0f}%, "
+            f"F2P-P={repo['f2p_post_fail_rate']:.0f}%, P2P={repo['p2p_fail_rate']:.0f}%"
+        )
+
+    # Distribution analysis
+    print("\n" + "=" * 120)
+    print("PERFECT PASS RATE DISTRIBUTION")
+    print("=" * 120)
+
+    buckets = {"0%": 0, "1-25%": 0, "26-50%": 0, "51-75%": 0, "76-99%": 0, "100%": 0}
+
+    for repo in repo_stats:
+        rate = repo["perfect_rate"]
+        if rate == 0:
+            buckets["0%"] += 1
+        elif rate <= 25:
+            buckets["1-25%"] += 1
+        elif rate <= 50:
+            buckets["26-50%"] += 1
+        elif rate <= 75:
+            buckets["51-75%"] += 1
+        elif rate < 100:
+            buckets["76-99%"] += 1
+        else:
+            buckets["100%"] += 1
+
+    for bucket, count in buckets.items():
+        print(f"{bucket:<10}: {count:>3} repositories")
+
+
+def main():
+    repo_stats = load_and_analyze_by_repo()
+    print_analysis(repo_stats)
+
+    # Save detailed results
+    with open("repo_perfect_pass_analysis.txt", "w") as f:
+        f.write(
+            "Repository,Total,Perfect,PerfectRate%,Errors,F2P-Initial-Fail,F2P-Post-Fail,P2P-Fail\n"
+        )
+        for repo in repo_stats:
+            f.write(
+                f"{repo['repo']},{repo['total']},{repo['perfect']},{repo['perfect_rate']:.1f},"
+                f"{repo['error_count']},{repo['f2p_initial_fail_count']},"
+                f"{repo['f2p_post_fail_count']},{repo['p2p_fail_count']}\n"
+            )
+
+    print("\n\nDetailed results saved to 'repo_perfect_pass_analysis.txt'")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/dev/swebench/analysis/analyze_results.py b/dev/swebench/analysis/analyze_results.py
new file mode 100644
index 00000000..5c34755d
--- /dev/null
+++ b/dev/swebench/analysis/analyze_results.py
@@ -0,0 +1,457 @@
+#!/usr/bin/env python3
+"""
+Comprehensive analysis of SWE-bench test results using Polars.
+Aggregates by instance_id, taking the last occurrence.
+"""
+
+import json
+import re
+from collections import Counter
+
+import polars as pl
+
+
+def load_test_results(
+    file_path: str, aggregate_by_instance: bool = True
+) -> pl.DataFrame:
+    """Load and aggregate test results by instance_id."""
+    # Read JSONL file and flatten the nested structure
+    flattened_data = []
+    with open(file_path, "r") as f:
+        for i, line in enumerate(f):
+            record = json.loads(line)
+            flattened = {
+                "instance_id": record.get("instance_id"),
+                "repo": record.get("repo"),
+                "timestamp": record.get("timestamp"),
+                "provider": record.get("provider"),
+                "error": record.get("error"),
+                "f2p_initial_failed": record.get("f2p_initial", {}).get("failed"),
+                "f2p_initial_passed": record.get("f2p_initial", {}).get("passed"),
+                "f2p_initial_error": record.get("f2p_initial", {}).get("error"),
+                "f2p_post_failed": record.get("f2p_post_patch", {}).get("failed"),
+                "f2p_post_passed": record.get("f2p_post_patch", {}).get("passed"),
+                "f2p_post_error": record.get("f2p_post_patch", {}).get("error"),
+                "p2p_failed": record.get("p2p", {}).get("failed"),
+                "p2p_passed": record.get("p2p", {}).get("passed"),
+                "p2p_error": record.get("p2p", {}).get("error"),
+                "_line_number": i,
+            }
+            flattened_data.append(flattened)
+
+    # Create DataFrame with explicit schema
+    schema = {
+        "instance_id": pl.Utf8,
+        "repo": pl.Utf8,
+        "timestamp": pl.Utf8,
+        "provider": pl.Utf8,
+        "error": pl.Utf8,
+        "f2p_initial_failed": pl.Int64,
+        "f2p_initial_passed": pl.Int64,
+        "f2p_initial_error": pl.Utf8,
+        "f2p_post_failed": pl.Int64,
+        "f2p_post_passed": pl.Int64,
+        "f2p_post_error": pl.Utf8,
+        "p2p_failed": pl.Int64,
+        "p2p_passed": pl.Int64,
+        "p2p_error": pl.Utf8,
+        "_line_number": pl.Int64,
+    }
+
+    df = pl.DataFrame(flattened_data, schema=schema)
+
+    if aggregate_by_instance:
+        # Take the last occurrence of each instance_id
+        df = df.sort("_line_number").group_by("instance_id").last()
+
+    return df.drop("_line_number")
+
+
+def print_basic_stats(df: pl.DataFrame):
+    """Print basic statistics about the dataset."""
+    print(f"\nTotal Unique Instances: {len(df):,}")
+
+    # Count instances with errors
+    has_error = df.filter(pl.col("error").is_not_null())
+    print(
+        f"Instances with top-level errors: {len(has_error):,} ({len(has_error) / len(df) * 100:.1f}%)"
+    )
+
+    # Count instances with test errors
+    has_test_error = df.filter(
+        (pl.col("f2p_initial_error").is_not_null())
+        | (pl.col("f2p_post_error").is_not_null())
+        | (pl.col("p2p_error").is_not_null())
+    )
+    print(
+        f"Instances with test errors: {len(has_test_error):,} ({len(has_test_error) / len(df) * 100:.1f}%)"
+    )
+
+
+def analyze_perfect_passes(df: pl.DataFrame) -> dict:
+    """Analyze instances that passed all tests perfectly."""
+    # Perfect means:
+    # 1. No errors at all
+    # 2. All F2P tests passed initially (as expected)
+    # 3. All F2P tests failed after patch (as expected)
+    # 4. All P2P tests passed (as expected)
+    perfect_passes = df.filter(
+        (pl.col("error").is_null())
+        & (pl.col("f2p_initial_error").is_null())
+        & (pl.col("f2p_post_error").is_null())
+        & (pl.col("p2p_error").is_null())
+        & (pl.col("f2p_initial_failed") == 0)  # All F2P should pass initially
+        & (pl.col("f2p_post_passed") == 0)  # All F2P should fail after patch
+        & (pl.col("p2p_failed") == 0)  # All P2P should pass
+    )
+
+    return {
+        "count": len(perfect_passes),
+        "percentage": len(perfect_passes) / len(df) * 100,
+        "instances": perfect_passes.select("instance_id").to_series().to_list(),
+    }
+
+
+def analyze_detailed_failure_modes(df: pl.DataFrame) -> dict:
+    """Analyze detailed failure modes and patterns."""
+    results = {}
+
+    # Categorize all instances
+    perfect = df.filter(
+        (pl.col("error").is_null())
+        & (pl.col("f2p_initial_error").is_null())
+        & (pl.col("f2p_post_error").is_null())
+        & (pl.col("p2p_error").is_null())
+        & (pl.col("f2p_initial_failed") == 0)  # All F2P should pass initially
+        & (pl.col("f2p_post_passed") == 0)  # All F2P should fail after patch
+        & (pl.col("p2p_failed") == 0)  # All P2P should pass
+    )
+
+    # Has any error
+    has_error = df.filter(pl.col("error").is_not_null())
+
+    # Test failures (no error but some tests failed)
+    test_failures = df.filter(
+        (pl.col("error").is_null())
+        & (
+            (
+                pl.col("f2p_initial_failed") > 0
+            )  # Some F2P failed initially (bad - should pass)
+            | (
+                pl.col("f2p_post_passed") > 0
+            )  # Some F2P passed after patch (bad - should fail)
+            | (pl.col("p2p_failed") > 0)  # Some P2P failed (bad)
+        )
+    )
+
+    # Categorize test failures
+    f2p_initial_wrong = test_failures.filter(pl.col("f2p_initial_failed") > 0)
+    f2p_post_wrong = test_failures.filter(pl.col("f2p_post_passed") > 0)
+    p2p_wrong = test_failures.filter(pl.col("p2p_failed") > 0)
+
+    # Test errors (errors during test execution)
+    test_errors = df.filter(
+        (pl.col("error").is_null())
+        & (
+            (pl.col("f2p_initial_error").is_not_null())
+            | (pl.col("f2p_post_error").is_not_null())
+            | (pl.col("p2p_error").is_not_null())
+        )
+    )
+
+    results["categories"] = {
+        "perfect": {"count": len(perfect), "pct": len(perfect) / len(df) * 100},
+        "has_error": {"count": len(has_error), "pct": len(has_error) / len(df) * 100},
+        "test_failures": {
+            "count": len(test_failures),
+            "pct": len(test_failures) / len(df) * 100,
+        },
+        "test_errors": {
+            "count": len(test_errors),
+            "pct": len(test_errors) / len(df) * 100,
+        },
+    }
+
+    results["test_failure_breakdown"] = {
+        "f2p_initial_failed_when_should_pass": len(f2p_initial_wrong),
+        "f2p_post_passed_when_should_fail": len(f2p_post_wrong),
+        "p2p_failed_when_should_pass": len(p2p_wrong),
+    }
+
+    # Analyze common error messages
+    error_messages = []
+    for col in ["error", "f2p_initial_error", "f2p_post_error", "p2p_error"]:
+        errors = df.filter(pl.col(col).is_not_null()).select(col).to_series().to_list()
+        error_messages.extend(errors)
+
+    # Extract error patterns
+    error_counter = Counter()
+    for error in error_messages:
+        if error:
+            # Try to extract the main error type
+            if "More than" in error and "tests" in error:
+                match = re.search(r"More than (\d+) tests", error)
+                if match:
+                    error_counter[f"More than {match.group(1)} tests"] += 1
+            elif "No available runners" in error:
+                error_counter["No available runners"] += 1
+            elif "timeout" in error.lower():
+                error_counter["Timeout error"] += 1
+            elif "pytest" in error:
+                # Extract pytest error type
+                if "not found" in error:
+                    error_counter["Pytest: file/test not found"] += 1
+                elif "FAILED" in error:
+                    error_counter["Pytest: test execution failed"] += 1
+                else:
+                    error_counter["Pytest: other error"] += 1
+            else:
+                # Try to get first line of error
+                first_line = error.split("\n")[0][:100]
+                error_counter[first_line] += 1
+
+    results["top_errors"] = error_counter.most_common(20)
+
+    return results
+
+
+def analyze_repositories(df: pl.DataFrame) -> dict:
+    """Analyze error rates by repository."""
+    # Extract repository name from instance_id
+    df = df.with_columns(
+        pl.col("instance_id").str.extract(r"^([^_]+)", 1).alias("repo")
+    )
+
+    # Calculate stats by repo
+    repo_stats = []
+    for repo in df.select("repo").unique().to_series().to_list():
+        if repo:
+            repo_df = df.filter(pl.col("repo") == repo)
+            total = len(repo_df)
+            with_errors = len(repo_df.filter(pl.col("error").is_not_null()))
+
+            repo_stats.append(
+                {
+                    "repo": repo,
+                    "total": total,
+                    "with_errors": with_errors,
+                    "error_rate": with_errors / total * 100 if total > 0 else 0,
+                }
+            )
+
+    # Sort by error rate
+    by_error_rate = sorted(repo_stats, key=lambda x: x["error_rate"], reverse=True)
+    by_count = sorted(repo_stats, key=lambda x: x["total"], reverse=True)
+
+    return {
+        "by_error_rate": [(r["repo"], r["error_rate"]) for r in by_error_rate],
+        "by_count": [(r["repo"], r["total"]) for r in by_count],
+        "full_stats": by_error_rate,
+    }
+
+
+def analyze_failure_patterns(df: pl.DataFrame) -> dict:
+    """Analyze specific failure patterns."""
+    # Count different types of failures
+    f2p_initial_failures = len(
+        df.filter((pl.col("error").is_null()) & (pl.col("f2p_initial_failed") > 0))
+    )
+
+    f2p_post_failures = len(
+        df.filter((pl.col("error").is_null()) & (pl.col("f2p_post_passed") > 0))
+    )
+
+    p2p_failures = len(
+        df.filter((pl.col("error").is_null()) & (pl.col("p2p_failed") > 0))
+    )
+
+    # Get specific instances with P2P failures
+    p2p_failure_instances = (
+        df.filter((pl.col("error").is_null()) & (pl.col("p2p_failed") > 0))
+        .select("instance_id")
+        .to_series()
+        .to_list()
+    )
+
+    return {
+        "f2p_initial_failures": f2p_initial_failures,
+        "f2p_post_failures": f2p_post_failures,
+        "p2p_failures": p2p_failures,
+        "p2p_failure_instances": p2p_failure_instances,
+    }
+
+
+def extract_unique_errors(df: pl.DataFrame):
+    """Extract and save unique error messages."""
+    all_errors = []
+
+    for col in ["error", "f2p_initial_error", "f2p_post_error", "p2p_error"]:
+        errors = df.filter(pl.col(col).is_not_null()).select(
+            [pl.col("instance_id"), pl.col(col).alias("error_msg")]
+        )
+        for row in errors.iter_rows(named=True):
+            all_errors.append((row["instance_id"], col, row["error_msg"]))
+
+    # Group by error message
+    error_groups = {}
+    for instance_id, error_type, error_msg in all_errors:
+        if error_msg not in error_groups:
+            error_groups[error_msg] = []
+        error_groups[error_msg].append((instance_id, error_type))
+
+    # Save to file
+    with open("unique_errors.txt", "w") as f:
+        f.write(f"Unique Error Messages: {len(error_groups)}\n")
+        f.write("=" * 80 + "\n\n")
+
+        # Sort by frequency
+        sorted_errors = sorted(
+            error_groups.items(), key=lambda x: len(x[1]), reverse=True
+        )
+
+        for error_msg, instances in sorted_errors:
+            f.write(f"Error ({len(instances)} occurrences):\n")
+            f.write("-" * 40 + "\n")
+            f.write(f"{error_msg}\n")
+            f.write("\nAffected instances:\n")
+            for inst, err_type in instances[:5]:  # Show first 5
+                f.write(f"  - {inst} ({err_type})\n")
+            if len(instances) > 5:
+                f.write(f"  ... and {len(instances) - 5} more\n")
+            f.write("\n" + "=" * 80 + "\n\n")
+
+
+def analyze_test_counts(df: pl.DataFrame) -> dict:
+    """Analyze instances with high test counts."""
+    # Look for errors mentioning test count limits
+    test_count_errors = df.filter(
+        pl.col("error").str.contains("More than.*tests")
+        | pl.col("f2p_initial_error").str.contains("More than.*tests")
+        | pl.col("f2p_post_error").str.contains("More than.*tests")
+        | pl.col("p2p_error").str.contains("More than.*tests")
+    )
+
+    # Extract test counts from error messages
+    high_test_instances = []
+    for row in test_count_errors.iter_rows(named=True):
+        instance_id = row["instance_id"]
+        for col in ["error", "f2p_initial_error", "f2p_post_error", "p2p_error"]:
+            if row[col] and "More than" in row[col]:
+                match = re.search(r"More than (\d+) tests", row[col])
+                if match:
+                    test_count = int(match.group(1))
+                    high_test_instances.append((instance_id, test_count))
+                    break
+
+    # Sort by test count
+    high_test_instances.sort(key=lambda x: x[1], reverse=True)
+
+    return {
+        "high_test_count_instances": high_test_instances,
+        "total_affected": len(test_count_errors),
+    }
+
+
+def main():
+    # Load data with aggregation
+    df = load_test_results("instance_test_results.jsonl", aggregate_by_instance=True)
+
+    # Basic stats
+    print("=" * 80)
+    print("SWE-bench Test Results Analysis (Latest Run Per Instance)")
+    print("=" * 80)
+    print_basic_stats(df)
+
+    # Detailed failure mode analysis
+    failure_modes = analyze_detailed_failure_modes(df)
+
+    print("\n" + "=" * 80)
+    print("INSTANCE CATEGORIZATION")
+    print("=" * 80)
+
+    for category, stats in failure_modes["categories"].items():
+        print(
+            f"{category.replace('_', ' ').title():25} {stats['count']:6,} ({stats['pct']:5.1f}%)"
+        )
+
+    print("\n" + "=" * 80)
+    print("PERFECT PASS ANALYSIS")
+    print("=" * 80)
+
+    # Perfect passes
+    perfect = analyze_perfect_passes(df)
+    print(
+        f"\nPerfect Passes: {perfect['count']:,} / {len(df):,} ({perfect['percentage']:.1f}%)"
+    )
+    print("\nPerfect means:")
+    print("  - No errors during sandbox creation or test execution")
+    print("  - All FAIL_TO_PASS tests passed initially (as expected)")
+    print(
+        "  - All FAIL_TO_PASS tests failed after patch (as expected - patch introduces bug)"
+    )
+    print("  - All PASS_TO_PASS tests passed (as expected)")
+
+    print("\n" + "=" * 80)
+    print("TEST FAILURE BREAKDOWN (No Errors, But Wrong Results)")
+    print("=" * 80)
+
+    for failure_type, count in failure_modes["test_failure_breakdown"].items():
+        print(f"{failure_type.replace('_', ' ').title():50} {count:6,}")
+
+    print("\n" + "=" * 80)
+    print("TOP ERROR PATTERNS")
+    print("=" * 80)
+
+    for i, (error, count) in enumerate(failure_modes["top_errors"], 1):
+        print(f"{i:2}. {error[:70]:70} {count:6,}")
+
+    # Repository analysis
+    repo_stats = analyze_repositories(df)
+    print("\n" + "=" * 80)
+    print("REPOSITORY ANALYSIS")
+    print("=" * 80)
+
+    print("\nTop 10 Repositories by Error Rate:")
+    for repo, rate in repo_stats["by_error_rate"][:10]:
+        total = df.filter(pl.col("instance_id").str.contains(repo)).height
+        errors = df.filter(
+            (pl.col("instance_id").str.contains(repo)) & (pl.col("error").is_not_null())
+        ).height
+        print(f"  {repo:40} {rate:5.1f}% ({errors}/{total})")
+
+    print("\n" + "=" * 80)
+    print("SPECIFIC INSTANCE ANALYSIS")
+    print("=" * 80)
+
+    # Find instances that consistently fail P2P
+    p2p_failures = df.filter((pl.col("error").is_null()) & (pl.col("p2p_failed") > 0))
+
+    if len(p2p_failures) > 0:
+        print(f"\nInstances with P2P failures: {len(p2p_failures)}")
+        # Group by repository
+        p2p_by_repo = Counter()
+        for instance in p2p_failures.select("instance_id").to_series().to_list():
+            repo = instance.split("__")[0]
+            p2p_by_repo[repo] += 1
+
+        print("\nP2P failures by repository:")
+        for repo, count in p2p_by_repo.most_common():
+            print(f"  {repo:40} {count:3} instances")
+
+    # Test count analysis
+    test_count_stats = analyze_test_counts(df)
+    if test_count_stats["high_test_count_instances"]:
+        print(
+            f"\n\nInstances with >3000 tests: {len(test_count_stats['high_test_count_instances'])}"
+        )
+        print(
+            f"Instances with >8000 tests: {len([i for i, c in test_count_stats['high_test_count_instances'] if c > 8000])}"
+        )
+
+    # Extract unique errors
+    extract_unique_errors(df)
+    print("\n\nDetailed error messages saved to 'unique_errors.txt'")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/dev/swebench/analysis/cleanup_daytona_sandboxes.py b/dev/swebench/analysis/cleanup_daytona_sandboxes.py
new file mode 100755
index 00000000..b7201e54
--- /dev/null
+++ b/dev/swebench/analysis/cleanup_daytona_sandboxes.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+"""
+Script to delete all Daytona sandboxes.
+Useful for cleaning up when disk quota is exceeded.
+"""
+
+import asyncio
+
+import daytona_sdk
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+async def cleanup_all_sandboxes():
+    """Delete all existing Daytona sandboxes."""
+    async with daytona_sdk.AsyncDaytona() as daytona:
+        # List all sandboxes
+        sandboxes = await daytona.list()
+
+        if not sandboxes:
+            print("No Daytona sandboxes found.")
+            return
+
+        print(f"Found {len(sandboxes)} Daytona sandbox(es) to delete...")
+
+        # Delete all sandboxes in parallel
+        delete_tasks = [sandbox.delete() for sandbox in sandboxes]
+        results = await asyncio.gather(*delete_tasks, return_exceptions=True)
+
+        # Count successes and failures
+        success_count = 0
+        failure_count = 0
+
+        for i, result in enumerate(results):
+            if isinstance(result, Exception):
+                print(f"  ❌ Failed to delete sandbox {i + 1}: {result}")
+                failure_count += 1
+            else:
+                success_count += 1
+
+        print("\nSummary:")
+        print(f"  ✅ Successfully deleted: {success_count}")
+        if failure_count > 0:
+            print(f"  ❌ Failed to delete: {failure_count}")
+
+        print("\nCleanup completed!")
+
+
+if __name__ == "__main__":
+    asyncio.run(cleanup_all_sandboxes())
diff --git a/dev/swebench/analysis/instance_test_results.jsonl b/dev/swebench/analysis/instance_test_results.jsonl
new file mode 100644
index 00000000..50832e28
--- /dev/null
+++ b/dev/swebench/analysis/instance_test_results.jsonl
@@ -0,0 +1,10981 @@
+{"instance_id": "pygments__pygments.27649ebb.pr_2651", "repo": "swesmith/pygments__pygments.27649ebb", "timestamp": "2025-07-10T19:01:33.569564", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 5048 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "python-jsonschema__jsonschema.93e0caa5.func_basic__yjd8950u", "repo": "swesmith/python-jsonschema__jsonschema.93e0caa5", "timestamp": "2025-07-10T19:01:33.570205", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 7225 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "kurtmckee__feedparser.cad965a3.lm_rewrite__av9fbunk", "repo": "swesmith/kurtmckee__feedparser.cad965a3", "timestamp": "2025-07-10T19:01:33.570661", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 4223 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "kurtmckee__feedparser.cad965a3.lm_rewrite__wvxss8rz", "repo": "swesmith/kurtmckee__feedparser.cad965a3", "timestamp": "2025-07-10T19:01:33.571538", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 4225 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "pygments__pygments.27649ebb.pr_2369", "repo": "swesmith/pygments__pygments.27649ebb", "timestamp": "2025-07-10T19:01:33.571700", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 5059 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "modin-project__modin.8c7799fd.lm_rewrite__29tgivpc", "repo": "swesmith/modin-project__modin.8c7799fd", "timestamp": "2025-07-10T19:01:33.573163", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 6849b356-8555-468f-a8df-198d9037f793 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "tobymao__sqlglot.036601ba.lm_rewrite__cfjfnvhf", "repo": "swesmith/tobymao__sqlglot.036601ba", "timestamp": "2025-07-10T19:01:33.572129", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox bb216a7f-c80d-4387-87ab-d09f353b9622 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "gruns__furl.da386f68.lm_rewrite__y903edew", "repo": "swesmith/gruns__furl.da386f68", "timestamp": "2025-07-10T19:01:33.573210", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox aab0af1f-43fa-450b-b7e5-a95c4f2a7c8e failed to start with state: error, error reason: No available runners"}
+{"instance_id": "sloria__environs.73c372df.lm_rewrite__t89wx7lp", "repo": "swesmith/sloria__environs.73c372df", "timestamp": "2025-07-10T19:01:33.571348", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 214a4aac-a362-4530-8696-364f0f053182 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "agronholm__typeguard.b6a7e438.func_basic__7q7zuw39", "repo": "swesmith/agronholm__typeguard.b6a7e438", "timestamp": "2025-07-10T19:01:33.573015", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 55c4e046-e454-4566-aa87-e7a7e410265b failed to start with state: error, error reason: No available runners"}
+{"instance_id": "python__mypy.e93f06ce.pr_10430", "repo": "swesmith/python__mypy.e93f06ce", "timestamp": "2025-07-10T19:01:33.572153", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 6ae6a228-65ee-48f3-9728-3c3acdbbeb04 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "gawel__pyquery.811cd048.combine_file__zc3d850c", "repo": "swesmith/gawel__pyquery.811cd048", "timestamp": "2025-07-10T19:01:33.572058", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox bc16bda3-0430-4b9e-9dd1-9277ab948b03 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "life4__textdistance.c3aca916.func_pm_ctrl_shuffle__asaa092v", "repo": "swesmith/life4__textdistance.c3aca916", "timestamp": "2025-07-10T19:01:33.573090", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 3fcfb811-8e15-4c75-b81a-e0b6827bd349 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "python-jsonschema__jsonschema.93e0caa5.lm_rewrite__n03efems", "repo": "swesmith/python-jsonschema__jsonschema.93e0caa5", "timestamp": "2025-07-10T19:01:35.354656", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 7191 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "sunpy__sunpy.f8edfd5c.combine_module__urmxyrfi", "repo": "swesmith/sunpy__sunpy.f8edfd5c", "timestamp": "2025-07-10T19:01:34.483806", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 28ab0b2b-23ce-4c73-971f-421cc7e0db8b failed to start with state: error, error reason: No available runners"}
+{"instance_id": "tobymao__sqlglot.036601ba.lm_rewrite__7prsfeeb", "repo": "swesmith/tobymao__sqlglot.036601ba", "timestamp": "2025-07-10T19:01:35.202932", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 52072b18-27e2-4960-a02e-2ff221c614ff failed to start with state: error, error reason: No available runners"}
+{"instance_id": "matthewwithanm__python-markdownify.6258f5c3.func_pm_remove_assign__lkj9fjf0", "repo": "swesmith/matthewwithanm__python-markdownify.6258f5c3", "timestamp": "2025-07-10T19:01:34.986916", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox a2086eb6-41bd-44fb-97eb-b64ec5530d76 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "Mimino666__langdetect.a1598f1a.func_pm_class_rm_funcs__842dr37d", "repo": "swesmith/Mimino666__langdetect.a1598f1a", "timestamp": "2025-07-10T19:01:33.569291", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 4, "error": null}, "f2p_post_patch": {"failed": 4, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 19, "error": null}}
+{"instance_id": "alecthomas__voluptuous.a7a55f83.lm_rewrite__9e2nb1af", "repo": "swesmith/alecthomas__voluptuous.a7a55f83", "timestamp": "2025-07-10T19:01:33.573231", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "gruns__furl.da386f68.lm_rewrite__g7mnoacj", "repo": "swesmith/gruns__furl.da386f68", "timestamp": "2025-07-10T19:01:52.413057", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox aa79b492-4a96-4794-83cc-4909b37ca59a failed to start with state: error, error reason: No available runners"}
+{"instance_id": "python-hyper__h11.bed0dd4a.func_basic__9n37b8v3", "repo": "swesmith/python-hyper__h11.bed0dd4a", "timestamp": "2025-07-10T19:01:33.569901", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 5, "error": null}, "f2p_post_patch": {"failed": 5, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 71, "error": null}}
+{"instance_id": "dask__dask.5f61e423.pr_10849", "repo": "swesmith/dask__dask.5f61e423", "timestamp": "2025-07-10T19:01:33.572604", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "Suor__funcy.207a7810.combine_file__k7uqr3fm", "repo": "swesmith/Suor__funcy.207a7810", "timestamp": "2025-07-10T19:01:33.571329", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 3, "error": null}, "f2p_post_patch": {"failed": 3, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 200, "error": null}}
+{"instance_id": "pyca__pyopenssl.04766a49.lm_rewrite__aotii0zj", "repo": "swesmith/pyca__pyopenssl.04766a49", "timestamp": "2025-07-10T19:01:52.784908", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 7c0475cb-3031-4876-b659-b2dfacb33926 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "theskumar__python-dotenv.2b8635b7.combine_module__r1pea5rl", "repo": "swesmith/theskumar__python-dotenv.2b8635b7", "timestamp": "2025-07-10T19:01:33.571652", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 6, "error": null}, "f2p_post_patch": {"failed": 6, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "conan-io__conan.86f29e13.pr_12889", "repo": "swesmith/conan-io__conan.86f29e13", "timestamp": "2025-07-10T19:01:33.573141", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 15, "error": null}}
+{"instance_id": "tkrajina__gpxpy.09fc46b3.func_pm_ctrl_shuffle__grix6fbc", "repo": "swesmith/tkrajina__gpxpy.09fc46b3", "timestamp": "2025-07-10T19:01:33.570016", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 2, "error": null}, "f2p_post_patch": {"failed": 2, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 141, "error": null}}
+{"instance_id": "python-hyper__h11.bed0dd4a.lm_rewrite__i45s4fz1", "repo": "swesmith/python-hyper__h11.bed0dd4a", "timestamp": "2025-07-10T19:01:53.145881", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 40da1884-9204-4cac-a606-6ad6b9c83dfb failed to start with state: error, error reason: No available runners"}
+{"instance_id": "tkrajina__gpxpy.09fc46b3.lm_rewrite__zkcxo20g", "repo": "swesmith/tkrajina__gpxpy.09fc46b3", "timestamp": "2025-07-10T19:01:33.570908", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 2, "error": null}, "f2p_post_patch": {"failed": 2, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 141, "error": null}}
+{"instance_id": "mozillazg__python-pinyin.e42dede5.func_pm_ctrl_shuffle__gx1fpj5d", "repo": "swesmith/mozillazg__python-pinyin.e42dede5", "timestamp": "2025-07-10T19:01:53.113855", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 7712d66a-bb13-4ad6-9de2-9de5d75f6457 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "kurtmckee__feedparser.cad965a3.lm_rewrite__sytdbk6z", "repo": "swesmith/kurtmckee__feedparser.cad965a3", "timestamp": "2025-07-10T19:01:53.446801", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 4201 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "tkrajina__gpxpy.09fc46b3.lm_rewrite__zh69lrln", "repo": "swesmith/tkrajina__gpxpy.09fc46b3", "timestamp": "2025-07-10T19:01:33.571518", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 142, "error": null}}
+{"instance_id": "mozilla__bleach.73871d76.lm_rewrite__7cli97ru", "repo": "swesmith/mozilla__bleach.73871d76", "timestamp": "2025-07-10T19:01:53.370873", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 32e4a264-1ad6-4f22-9d5c-f598bce29f48 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "theskumar__python-dotenv.2b8635b7.lm_rewrite__42pcrrbl", "repo": "swesmith/theskumar__python-dotenv.2b8635b7", "timestamp": "2025-07-10T19:01:33.573326", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 80, "error": null}}
+{"instance_id": "alecthomas__voluptuous.a7a55f83.lm_rewrite__7clrnhsi", "repo": "swesmith/alecthomas__voluptuous.a7a55f83", "timestamp": "2025-07-10T19:01:33.571417", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 143, "error": null}}
+{"instance_id": "sunpy__sunpy.f8edfd5c.combine_module__5016abf8", "repo": "swesmith/sunpy__sunpy.f8edfd5c", "timestamp": "2025-07-10T19:01:53.446875", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 8e1fc84c-dd97-4bc8-94b8-6ee2f2e9a012 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "spulec__freezegun.5f171db0.func_pm_ctrl_invert_if__sl25599x", "repo": "swesmith/spulec__freezegun.5f171db0", "timestamp": "2025-07-10T19:01:53.751826", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox da065746-533b-4e85-b622-f981b90fca5b failed to start with state: error, error reason: No available runners"}
+{"instance_id": "theskumar__python-dotenv.2b8635b7.func_basic__2pl82xgf", "repo": "swesmith/theskumar__python-dotenv.2b8635b7", "timestamp": "2025-07-10T19:01:33.572533", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 4, "error": null}, "f2p_post_patch": {"failed": 4, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "getmoto__moto.694ce1f4.pr_5106", "repo": "swesmith/getmoto__moto.694ce1f4", "timestamp": "2025-07-10T19:01:33.569536", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 8, "error": null}, "f2p_post_patch": {"failed": 8, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "pdfminer__pdfminer.six.1a8bd2f7.func_pm_remove_loop__q0fmi7se", "repo": "swesmith/pdfminer__pdfminer.six.1a8bd2f7", "timestamp": "2025-07-10T19:01:33.570070", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 4, "error": null}, "f2p_post_patch": {"failed": 4, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 165, "error": null}}
+{"instance_id": "pdfminer__pdfminer.six.1a8bd2f7.lm_rewrite__0d083wie", "repo": "swesmith/pdfminer__pdfminer.six.1a8bd2f7", "timestamp": "2025-07-10T19:01:33.571603", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 24, "error": null}, "f2p_post_patch": {"failed": 24, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 145, "error": null}}
+{"instance_id": "luozhouyang__python-string-similarity.115acaac.func_basic__6nnrxgue", "repo": "swesmith/luozhouyang__python-string-similarity.115acaac", "timestamp": "2025-07-10T19:01:33.572270", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 17, "error": null}}
+{"instance_id": "mahmoud__boltons.3bfcfdd0.lm_rewrite__5rqvj4sv", "repo": "swesmith/mahmoud__boltons.3bfcfdd0", "timestamp": "2025-07-10T19:01:33.572080", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 411, "error": null}}
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+{"instance_id": "iterative__dvc.1d6ea681.pr_7479", "repo": "swesmith/iterative__dvc.1d6ea681", "timestamp": "2025-07-10T19:01:52.722296", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "getmoto__moto.694ce1f4.pr_5011", "repo": "swesmith/getmoto__moto.694ce1f4", "timestamp": "2025-07-10T19:01:54.658028", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 13, "error": null}, "f2p_post_patch": {"failed": 13, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "bottlepy__bottle.a8dfef30.lm_rewrite__s39f8hoi", "repo": "swesmith/bottlepy__bottle.a8dfef30", "timestamp": "2025-07-10T19:01:33.570832", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 12, "error": null}, "f2p_post_patch": {"failed": 12, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
+{"instance_id": "django__daphne.32ac73e1.func_basic__ilpp3d9z", "repo": "swesmith/django__daphne.32ac73e1", "timestamp": "2025-07-10T19:01:33.570724", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 62, "error": null}}
+{"instance_id": "HIPS__autograd.ac044f0d.lm_rewrite__kx535eel", "repo": "swesmith/HIPS__autograd.ac044f0d", "timestamp": "2025-07-10T19:01:54.936185", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
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+{"instance_id": "gruns__icecream.f76fef56.combine_file__g2xikt5k", "repo": "swesmith/gruns__icecream.f76fef56", "timestamp": "2025-07-10T19:02:13.187757", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox 558de513-ea82-4139-aa92-e1447df83a92 failed to start with state: error, error reason: No available runners"}
+{"instance_id": "Suor__funcy.207a7810.combine_file__lu54fx6d", "repo": "swesmith/Suor__funcy.207a7810", "timestamp": "2025-07-10T19:01:53.845331", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 16, "error": null}, "f2p_post_patch": {"failed": 16, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 187, "error": null}}
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+{"instance_id": "dask__dask.5f61e423.pr_8501", "repo": "swesmith/dask__dask.5f61e423", "timestamp": "2025-07-10T19:01:52.861212", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 10, "error": null}}
+{"instance_id": "iterative__dvc.1d6ea681.pr_8063", "repo": "swesmith/iterative__dvc.1d6ea681", "timestamp": "2025-07-10T19:01:53.445480", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 0, "error": null}, "f2p_post_patch": {"failed": 0, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 0, "error": null}}
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+{"instance_id": "marshmallow-code__apispec.8b421526.lm_rewrite__3yeskox9", "repo": "swesmith/marshmallow-code__apispec.8b421526", "timestamp": "2025-07-10T19:01:53.505173", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 3, "error": null}, "f2p_post_patch": {"failed": 2, "passed": 1, "error": null}, "p2p": {"failed": 10, "passed": 556, "error": null}}
+{"instance_id": "cknd__stackprinter.219fcc52.lm_rewrite__h88769hx", "repo": "swesmith/cknd__stackprinter.219fcc52", "timestamp": "2025-07-10T19:01:57.731840", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 3, "error": null}, "f2p_post_patch": {"failed": 3, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 4, "error": null}}
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+{"instance_id": "scanny__python-pptx.278b47b1.func_pm_ctrl_shuffle__8u5gz5v4", "repo": "swesmith/scanny__python-pptx.278b47b1", "timestamp": "2025-07-10T19:01:33.572699", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 1, "error": null}, "f2p_post_patch": {"failed": 1, "passed": 0, "error": null}, "p2p": {"failed": 0, "passed": 2656, "error": null}}
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+{"instance_id": "pygments__pygments.27649ebb.pr_2507", "repo": "swesmith/pygments__pygments.27649ebb", "timestamp": "2025-07-10T19:02:15.641654", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 5018 PASS_TO_PASS tests (system limits)"}
+{"instance_id": "pygments__pygments.27649ebb.lm_rewrite__y9bb4fcd", "repo": "swesmith/pygments__pygments.27649ebb", "timestamp": "2025-07-10T19:02:15.641727", "provider": "daytona", "f2p_initial": {"failed": null, "passed": null, "error": null}, "f2p_post_patch": {"failed": null, "passed": null, "error": null}, "p2p": {"failed": null, "passed": null, "error": null}, "error": "Skipped: 5055 PASS_TO_PASS tests (system limits)"}
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+{"instance_id": "marshmallow-code__webargs.dbde72fe.combine_file__rck88xoa", "repo": "swesmith/marshmallow-code__webargs.dbde72fe", "timestamp": "2025-07-10T19:01:33.570316", "provider": "daytona", "f2p_initial": {"failed": 0, "passed": 5, "error": null}, "f2p_post_patch": {"failed": 5, "passed": 0, "error": null}, "p2p": {"failed": 11, "passed": 432, "error": null}}
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diff --git a/dev/swebench/analysis/repo_perfect_pass_details.csv b/dev/swebench/analysis/repo_perfect_pass_details.csv
new file mode 100644
index 00000000..8da913a8
--- /dev/null
+++ b/dev/swebench/analysis/repo_perfect_pass_details.csv
@@ -0,0 +1,106 @@
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diff --git a/dev/swebench/analysis/run_instance_tests.py b/dev/swebench/analysis/run_instance_tests.py
new file mode 100755
index 00000000..fc7638b5
--- /dev/null
+++ b/dev/swebench/analysis/run_instance_tests.py
@@ -0,0 +1,232 @@
+#!/usr/bin/env python3
+"""
+Script to run tests on SWE-bench instances and log results to JSONL.
+Runs up to 256 sandboxes concurrently using the Daytona provider.
+"""
+
+import asyncio
+import json
+import os
+from datetime import datetime
+from typing import Any, Dict
+
+import daytona_sdk
+from dotenv import load_dotenv
+from instances import Instance, as_instances_iter, get_filtered_swe_smith_instances_df
+from sandbox.new import new_sandbox
+from sandbox.sandbox import Provider
+
+load_dotenv()
+
+# Configuration
+RESULTS_FILE = "instance_test_results.jsonl"
+MAX_CONCURRENT_SANDBOXES = 128
+PROVIDER: Provider = "daytona"
+
+
+async def cleanup_all_sandboxes() -> None:
+    """Delete all existing Daytona sandboxes to prevent disk quota issues."""
+    async with daytona_sdk.AsyncDaytona() as daytona:
+        sandboxes = await daytona.list()
+
+        if sandboxes:
+            print(f"Cleaning up {len(sandboxes)} existing Daytona sandbox(es)...")
+            delete_tasks = [sandbox.delete() for sandbox in sandboxes]
+            results = await asyncio.gather(*delete_tasks, return_exceptions=True)
+
+            success_count = sum(1 for r in results if not isinstance(r, Exception))
+            failure_count = sum(1 for r in results if isinstance(r, Exception))
+
+            print(f"  Deleted: {success_count} sandbox(es)")
+            if failure_count > 0:
+                print(f"  Failed: {failure_count} sandbox(es)")
+
+
+async def load_existing_results() -> tuple[set[str], set[str]]:
+    """Load existing results and identify perfect passes to skip.
+
+    Returns:
+        - Set of instance IDs that passed perfectly (to skip)
+        - Set of all instance IDs that have been attempted (for stats)
+    """
+    perfect_passes = set()
+    all_instances = set()
+
+    if not os.path.exists(RESULTS_FILE):
+        return perfect_passes, all_instances
+
+    try:
+        with open(RESULTS_FILE, "r") as f:
+            for line in f:
+                try:
+                    result = json.loads(line.strip())
+                    instance_id = result.get("instance_id")
+                    if instance_id:
+                        all_instances.add(instance_id)
+
+                        # Check if this is a perfect pass
+                        if (
+                            not result.get("error")
+                            and result.get("f2p_initial", {}).get("failed") == 0
+                            and result.get("f2p_initial", {}).get("passed") is not None
+                            and result.get("f2p_post_patch", {}).get("passed") == 0
+                            and result.get("f2p_post_patch", {}).get("failed")
+                            is not None
+                            and result.get("p2p", {}).get("failed") == 0
+                            and result.get("p2p", {}).get("passed") is not None
+                        ):
+                            perfect_passes.add(instance_id)
+                except json.JSONDecodeError:
+                    continue
+    except Exception as e:
+        print(f"Error loading existing results: {e}")
+
+    return perfect_passes, all_instances
+
+
+async def run_instance_tests(instance: Instance) -> Dict[str, Any]:
+    """Run tests for a single instance and return results."""
+    instance_id = instance["instance_id"]
+    print(f"Starting tests for {instance_id}")
+
+    result = {
+        "instance_id": instance_id,
+        "repo": instance["repo"],
+        "timestamp": datetime.utcnow().isoformat(),
+        "provider": PROVIDER,
+        "f2p_initial": {"failed": None, "passed": None, "error": None},
+        "f2p_post_patch": {"failed": None, "passed": None, "error": None},
+        "p2p": {"failed": None, "passed": None, "error": None},
+    }
+
+    # Calculate dynamic timeout based on number of tests
+    base_timeout = 120  # Base time for dependency installation
+    per_test_time = 0.05  # Per-test time
+
+    # Skip instances with extreme test counts
+    if len(instance["PASS_TO_PASS"]) > 8000:
+        result["error"] = (
+            f"Skipped: {len(instance['PASS_TO_PASS'])} PASS_TO_PASS tests (system limits)"
+        )
+        return result
+
+    fail_to_pass_timeout = int(
+        base_timeout + len(instance["FAIL_TO_PASS"]) * per_test_time
+    )
+    pass_to_pass_timeout = int(
+        base_timeout + len(instance["PASS_TO_PASS"]) * per_test_time
+    )
+
+    try:
+        async with new_sandbox(
+            image=instance["image_name"], provider=PROVIDER
+        ) as sandbox:
+            # Run initial FAIL_TO_PASS tests (should pass without patch)
+            try:
+                failed, passed = await sandbox.run_tests(
+                    instance["FAIL_TO_PASS"], fail_to_pass_timeout
+                )
+                result["f2p_initial"]["failed"] = failed
+                result["f2p_initial"]["passed"] = passed
+            except Exception as e:
+                result["f2p_initial"]["error"] = str(e)
+
+            # Apply patch
+            try:
+                await sandbox.apply_patch(instance["patch"], 10)
+
+                # Run FAIL_TO_PASS tests after patch (should fail)
+                try:
+                    failed, passed = await sandbox.run_tests(
+                        instance["FAIL_TO_PASS"], fail_to_pass_timeout
+                    )
+                    result["f2p_post_patch"]["failed"] = failed
+                    result["f2p_post_patch"]["passed"] = passed
+                except Exception as e:
+                    result["f2p_post_patch"]["error"] = str(e)
+
+                # Run PASS_TO_PASS tests (should pass)
+                try:
+                    failed, passed = await sandbox.run_tests(
+                        instance["PASS_TO_PASS"], pass_to_pass_timeout
+                    )
+                    result["p2p"]["failed"] = failed
+                    result["p2p"]["passed"] = passed
+                except Exception as e:
+                    result["p2p"]["error"] = str(e)
+
+            except Exception as e:
+                result["error"] = f"Failed to apply patch: {e}"
+
+    except Exception as e:
+        result["error"] = f"Sandbox error: {e}"
+
+    print(f"Completed tests for {instance_id}")
+    return result
+
+
+async def append_result(result: Dict[str, Any]) -> None:
+    """Append a result to the JSONL file."""
+    with open(RESULTS_FILE, "a") as f:
+        f.write(json.dumps(result) + "\n")
+
+
+async def process_instance(instance: Instance, semaphore: asyncio.Semaphore) -> None:
+    """Process a single instance with semaphore for concurrency control."""
+    async with semaphore:
+        try:
+            result = await run_instance_tests(instance)
+            await append_result(result)
+        except Exception as e:
+            print(f"Error processing {instance['instance_id']}: {e}")
+            error_result = {
+                "instance_id": instance["instance_id"],
+                "timestamp": datetime.utcnow().isoformat(),
+                "error": str(e),
+            }
+            await append_result(error_result)
+
+
+async def main():
+    """Main function to run tests on all instances."""
+    # Clean up any existing sandboxes first
+    await cleanup_all_sandboxes()
+
+    # Load existing results
+    perfect_passes, all_attempted = await load_existing_results()
+    print(f"Found {len(perfect_passes)} perfect passes to skip")
+    print(f"Found {len(all_attempted)} total attempted instances")
+
+    # Get all instances
+    instances_df = get_filtered_swe_smith_instances_df()
+    all_instances = list(as_instances_iter(instances_df))
+
+    # Filter out only perfect passes (we'll retry failures and errors)
+    instances_to_run = [
+        inst for inst in all_instances if inst["instance_id"] not in perfect_passes
+    ]
+
+    print(f"Total instances: {len(all_instances)}")
+    print(f"Instances to run: {len(instances_to_run)}")
+
+    if not instances_to_run:
+        print("All instances have been processed!")
+        return
+
+    # Create semaphore for concurrency control
+    semaphore = asyncio.Semaphore(MAX_CONCURRENT_SANDBOXES)
+
+    # Create tasks for all instances
+    tasks = [process_instance(instance, semaphore) for instance in instances_to_run]
+
+    # Run all tasks concurrently
+    print(
+        f"Starting concurrent execution with up to {MAX_CONCURRENT_SANDBOXES} sandboxes..."
+    )
+    await asyncio.gather(*tasks)
+
+    print("All tests completed!")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/dev/swebench/analysis/run_tests.log b/dev/swebench/analysis/run_tests.log
new file mode 100644
index 00000000..acb2a54c
--- /dev/null
+++ b/dev/swebench/analysis/run_tests.log
@@ -0,0 +1,16596 @@
+warning: `VIRTUAL_ENV=/home/sky/sky_workdir/.venv` does not match the project environment path `.venv` and will be ignored; use `--active` to target the active environment instead
+Cleaning up 1 existing Daytona sandbox(es)...
+  Deleted: 0 sandbox(es)
+  Failed: 1 sandbox(es)
+Found 0 already completed instances
+Total instances: 8480
+Instances to run: 8480
+Starting concurrent execution with up to 128 sandboxes...
+Starting tests for Mimino666__langdetect.a1598f1a.func_pm_class_rm_funcs__842dr37d
+Starting tests for pydata__patsy.a5d16484.lm_rewrite__xuo1597r
+Starting tests for pytest-dev__iniconfig.16793ead.combine_module__494jqa5j
+Starting tests for getmoto__moto.694ce1f4.pr_5106
+Starting tests for pygments__pygments.27649ebb.pr_2651
+Starting tests for scrapy__scrapy.35212ec5.pr_6374
+Starting tests for conan-io__conan.86f29e13.pr_12464
+Starting tests for lincolnloop__python-qrcode.456b01d4.func_basic__1dk546ts
+Starting tests for python-hyper__h11.bed0dd4a.func_basic__9n37b8v3
+Starting tests for benoitc__gunicorn.bacbf8aa.lm_rewrite__ktwwjwby
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+Starting tests for marshmallow-code__webargs.dbde72fe.combine_file__rck88xoa
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+Starting tests for Cog-Creators__Red-DiscordBot.33e0eac7.combine_module__rizvz76q
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+Starting tests for iterative__dvc.1d6ea681.pr_8436
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+Starting tests for rubik__radon.54b88e58.combine_file__sg0dlvxc
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+Starting tests for django__channels.a144b4b8.func_basic__d3xp3lwl
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+Starting tests for rsalmei__alive-progress.35853799.func_basic__b49dwhak
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+Completed tests for tkrajina__gpxpy.09fc46b3.lm_rewrite__xmiv50a6
+Completed tests for pyutils__line_profiler.a646bf0f.combine_module__y5cyn4s7
+Completed tests for weaveworks__grafanalib.5c3b17ed.combine_file__3z6ydz8b
+Completed tests for google__textfsm.c31b6007.combine_file__tr1uv5c0
+Completed tests for getmoto__moto.694ce1f4.pr_6508
+Completed tests for sqlfluff__sqlfluff.50a1c4b6.lm_rewrite__jdgt5c9s
+Completed tests for buriy__python-readability.40256f40.func_basic__hejjm67s
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__sul251l3
+Completed tests for python-hyper__h11.bed0dd4a.lm_rewrite__sknw8cg8
+Completed tests for pygments__pygments.27649ebb.pr_2428
+Completed tests for davidhalter__parso.338a5760.lm_rewrite__hghjz115
+Completed tests for python-openxml__python-docx.0cf6d71f.combine_file__ygn13uil
+Completed tests for seperman__deepdiff.ed252022.lm_rewrite__jcu1p1t5
+Completed tests for mahmoud__boltons.3bfcfdd0.combine_file__aoaokdmx
+Completed tests for gawel__pyquery.811cd048.lm_rewrite__011t8oh6
+Completed tests for agronholm__typeguard.b6a7e438.func_pm_remove_cond__ehyhh6nv
+Completed tests for marshmallow-code__webargs.dbde72fe.combine_file__nz2krewn
+Completed tests for python-openxml__python-docx.0cf6d71f.lm_rewrite__mpj44qbs
+Completed tests for adrienverge__yamllint.8513d9b9.lm_rewrite__agc7y7gv
+Completed tests for getmoto__moto.694ce1f4.pr_5515
+Completed tests for oauthlib__oauthlib.1fd52536.lm_rewrite__2nmqdrrp
+Completed tests for lepture__mistune.bf54ef67.lm_rewrite__j6nxx0l5
+Completed tests for jaraco__inflect.c079a96a.func_pm_op_swap__byoaxo72
+Completed tests for seperman__deepdiff.ed252022.lm_rewrite__zimq23ld
+Completed tests for pydata__patsy.a5d16484.lm_rewrite__q4e1z2xw
+Completed tests for seperman__deepdiff.ed252022.pr_390
+Completed tests for seperman__deepdiff.ed252022.func_pm_remove_cond__zfum8cf3
+Completed tests for seperman__deepdiff.ed252022.lm_rewrite__xl4q8vxt
+Completed tests for sunpy__sunpy.f8edfd5c.combine_module__pxe28dkw
+Completed tests for conan-io__conan.86f29e13.pr_16038
+Completed tests for jsvine__pdfplumber.02ff4313.lm_rewrite__j1wfkmoh
+Completed tests for conan-io__conan.86f29e13.pr_13004
+Completed tests for scrapy__scrapy.35212ec5.func_pm_ctrl_shuffle__oudt5j8h
+Completed tests for joke2k__faker.8b401a7d.lm_rewrite__st05b3ms
+Completed tests for django-money__django-money.835c1ab8.func_basic__himjwidn
+Completed tests for dask__dask.5f61e423.pr_7191
+Completed tests for tweepy__tweepy.91a41c6e.lm_rewrite__d7fgi1ci
+Completed tests for amueller__word_cloud.ec24191c.func_basic__6gccpbhi
+Completed tests for amueller__word_cloud.ec24191c.func_pm_ctrl_shuffle__6n66e1l1
+Completed tests for arrow-py__arrow.1d70d009.lm_rewrite__kfjmv6sb
+Completed tests for marshmallow-code__webargs.dbde72fe.combine_module__8qynwkf7
+Completed tests for dask__dask.5f61e423.lm_rewrite__q6fqwinv
+Completed tests for Project-MONAI__MONAI.a09c1f08.func_pm_remove_assign__90ni1fns
+Completed tests for cantools__cantools.0c6a7871.lm_rewrite__yz71a8cb
+Completed tests for joke2k__faker.8b401a7d.lm_rewrite__dhtsf2hk
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_5405
+Completed tests for conan-io__conan.86f29e13.pr_13109
+Completed tests for joke2k__faker.8b401a7d.lm_rewrite__y2qfq0da
+Completed tests for pydata__patsy.a5d16484.func_basic__4dyjfuts
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__0e2l8brm
+Completed tests for pyparsing__pyparsing.533adf47.combine_file__l9h9sd0m
+Completed tests for conan-io__conan.86f29e13.pr_14673
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__xqtgiskq
+Completed tests for pydata__patsy.a5d16484.func_pm_ctrl_invert_if__une2tj06
+Completed tests for dask__dask.5f61e423.pr_10320
+Completed tests for tobymao__sqlglot.036601ba.lm_rewrite__aaducvrr
+Completed tests for conan-io__conan.86f29e13.pr_17052
+Completed tests for facebookresearch__fvcore.a491d5b9.lm_rewrite__eb9yluzu
+Completed tests for life4__textdistance.c3aca916.func_pm_op_break_chains__v5r5da7o
+Completed tests for facebookresearch__fvcore.a491d5b9.func_basic__9v6fbo43
+Completed tests for getnikola__nikola.0f4c230e.func_pm_ctrl_shuffle__8dv3hfh7
+Completed tests for jsvine__pdfplumber.02ff4313.lm_rewrite__jfgevnw1
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__d7sbe6yx
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__j2sm3lbk
+Completed tests for facebookresearch__fvcore.a491d5b9.func_basic__j5myi9rw
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_5350
+Completed tests for dask__dask.5f61e423.func_pm_op_swap__8riwm22r
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_7187
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_4745
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_4402
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__iu5dzytz
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_4583
+Completed tests for facebookresearch__fvcore.a491d5b9.lm_rewrite__7mpr1vrp
+Completed tests for Project-MONAI__MONAI.a09c1f08.combine_module__z8r8jyrk
+Completed tests for facebookresearch__fvcore.a491d5b9.combine_file__0e71vue2
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__xf99iyzn
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__tveptbxt
+Completed tests for tobymao__sqlglot.036601ba.lm_rewrite__hqb9f41y
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_6034
+Completed tests for getnikola__nikola.0f4c230e.combine_module__7m3d5u7t
+Completed tests for Project-MONAI__MONAI.a09c1f08.combine_module__4mur1wne
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__roex7dhi
+Completed tests for Project-MONAI__MONAI.a09c1f08.combine_module__lmqoh3l5
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__3boujiaj
+Completed tests for facebookresearch__fvcore.a491d5b9.lm_rewrite__m0xnjits
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__x9mlihlq
+Completed tests for Project-MONAI__MONAI.a09c1f08.combine_module__wbr5gtkj
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__1sq7j11i
+Completed tests for conan-io__conan.86f29e13.pr_17444
+Completed tests for facebookresearch__fvcore.a491d5b9.lm_rewrite__dg0egswn
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__ouutt1g0
+Completed tests for conan-io__conan.86f29e13.lm_rewrite__9dp34dn6
+Completed tests for facebookresearch__fvcore.a491d5b9.combine_module__qhmxuusv
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__vop6zr3r
+Completed tests for getnikola__nikola.0f4c230e.lm_rewrite__mnwy4oou
+Completed tests for tobymao__sqlglot.036601ba.lm_rewrite__w3nmrc62
+Completed tests for tobymao__sqlglot.036601ba.lm_rewrite__ld4dbcvw
+Completed tests for Project-MONAI__MONAI.a09c1f08.lm_rewrite__qvdzqoeu
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_6009
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_4692
+Completed tests for Project-MONAI__MONAI.a09c1f08.func_pm_remove_assign__70jyz2gr
+Completed tests for Project-MONAI__MONAI.a09c1f08.pr_5066
+Completed tests for Project-MONAI__MONAI.a09c1f08.combine_module__cgg26f99
+All tests completed!
+Unclosed client session
+client_session: <aiohttp.client.ClientSession object at 0x7065b0f3c820>
+Unclosed connector
+connections: ['deque([(<aiohttp.client_proto.ResponseHandler object at 0x7065b0444940>, 2948489.68905227), (<aiohttp.client_proto.ResponseHandler object at 0x706568404c40>, 2948489.715189751)])']
+connector: <aiohttp.connector.TCPConnector object at 0x7065b0f3c760>
diff --git a/dev/swebench/analysis/unique_errors.txt b/dev/swebench/analysis/unique_errors.txt
new file mode 100644
index 00000000..70db4ba7
--- /dev/null
+++ b/dev/swebench/analysis/unique_errors.txt
@@ -0,0 +1,92 @@
+Unique Error Messages: 7
+================================================================================
+
+Error (76 occurrences):
+----------------------------------------
+Failed to execute command: <html>
+<head><title>504 Gateway Time-out</title></head>
+<body>
+<center><h1>504 Gateway Time-out</h1></center>
+</body>
+</html>
+
+
+Affected instances:
+  - conan-io__conan.86f29e13.lm_rewrite__9dp34dn6 (f2p_initial_error)
+  - sqlfluff__sqlfluff.50a1c4b6.func_pm_ctrl_invert_if__w8e3m8ag (f2p_initial_error)
+  - tobymao__sqlglot.036601ba.lm_rewrite__jfj6trkz (f2p_initial_error)
+  - conan-io__conan.86f29e13.pr_10717 (f2p_initial_error)
+  - tobymao__sqlglot.036601ba.lm_rewrite__4x4jcxkb (f2p_initial_error)
+  ... and 71 more
+
+================================================================================
+
+Error (64 occurrences):
+----------------------------------------
+Failed to execute command: command execution timeout
+
+Affected instances:
+  - getnikola__nikola.0f4c230e.lm_rewrite__ydwl8yud (f2p_initial_error)
+  - getnikola__nikola.0f4c230e.combine_module__6cabwzd7 (f2p_initial_error)
+  - getnikola__nikola.0f4c230e.lm_rewrite__6gp9a2cp (f2p_initial_error)
+  - getnikola__nikola.0f4c230e.lm_rewrite__mnwy4oou (f2p_initial_error)
+  - getnikola__nikola.0f4c230e.lm_rewrite__qjc66i7w (f2p_initial_error)
+  ... and 59 more
+
+================================================================================
+
+Error (31 occurrences):
+----------------------------------------
+Failed to execute command: 
+
+Affected instances:
+  - getnikola__nikola.0f4c230e.combine_module__nhjk0grt (f2p_initial_error)
+  - tobymao__sqlglot.036601ba.lm_rewrite__fpqolxwl (f2p_initial_error)
+  - tobymao__sqlglot.036601ba.lm_rewrite__wz366ul0 (f2p_post_error)
+  - tobymao__sqlglot.036601ba.lm_rewrite__aaducvrr (f2p_post_error)
+  - getnikola__nikola.0f4c230e.combine_module__nhjk0grt (f2p_post_error)
+  ... and 26 more
+
+================================================================================
+
+Error (10 occurrences):
+----------------------------------------
+Sandbox error: Failed to create sandbox: Failed to create and start sandbox within 60 seconds timeout period.
+
+Affected instances:
+  - facebookresearch__fvcore.a491d5b9.func_pm_ctrl_shuffle__kd43vy4l (error)
+  - pygments__pygments.27649ebb.pr_2659 (error)
+  - pygments__pygments.27649ebb.lm_rewrite__2s9fgj0c (error)
+  - python-trio__trio.cfbbe2c1.lm_rewrite__uhrd1ebw (error)
+  - pygments__pygments.27649ebb.combine_file__x44f9cqe (error)
+  ... and 5 more
+
+================================================================================
+
+Error (1 occurrences):
+----------------------------------------
+Failed to apply patch: Failed to execute command: command execution timeout
+
+Affected instances:
+  - Project-MONAI__MONAI.a09c1f08.func_pm_remove_assign__70jyz2gr (error)
+
+================================================================================
+
+Error (1 occurrences):
+----------------------------------------
+Sandbox error: Failed to create sandbox: Failure during waiting for sandbox to start: Sandbox cc8cbe9c-0846-4fa9-8df1-b8663b8dadce failed to start with state: error, error reason: timeout waiting for daemon to start
+
+Affected instances:
+  - sunpy__sunpy.f8edfd5c.lm_rewrite__t1d7mg0t (error)
+
+================================================================================
+
+Error (1 occurrences):
+----------------------------------------
+Failed to apply patch: Failed to execute command: Missing parameter name at 6: https://git.new/pathToRegexpError
+
+Affected instances:
+  - mozilla__bleach.73871d76.lm_rewrite__01ahwe6g (error)
+
+================================================================================
+
diff --git a/dev/swebench/art_style_rollout.py b/dev/swebench/art_style_rollout.py
new file mode 100644
index 00000000..cf09882a
--- /dev/null
+++ b/dev/swebench/art_style_rollout.py
@@ -0,0 +1,370 @@
+import json
+import traceback
+
+from instances import Instance
+from openai.types.chat import (
+    ChatCompletionToolMessageParam,
+    ChatCompletionToolParam,
+)
+from pydantic import BaseModel
+from sandbox import Sandbox
+
+import art
+
+
+class ARTModelConfig(BaseModel):
+    """Configuration for ART-style models."""
+
+    max_steps: int = 40
+    max_output_length: int = 1000
+    temperature: float = 1.0
+    system_prompt: str = (
+        "You are a helpful assistant that can interact with a computer to solve tasks."
+    )
+    instance_prompt_template: str = """
+<uploaded_files>
+{working_dir}
+</uploaded_files>
+I've uploaded a python code repository in the directory {working_dir}. Consider the following PR description:
+
+<pr_description>
+{problem_statement}
+</pr_description>
+
+Can you help me implement the necessary changes to the repository so that the requirements specified in the <pr_description> are met?
+I've already taken care of all changes to any of the test files described in the <pr_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!
+Your task is to make the minimal changes to non-tests files in the {working_dir} directory to ensure the <pr_description> is satisfied.
+Follow these steps to resolve the issue:
+1. As a first step, it might be a good idea to find and read code relevant to the <pr_description>
+2. Create a script to reproduce the error and execute it with `python <filename.py>` using the bash tool, to confirm the error
+3. Edit the sourcecode of the repo to resolve the issue
+4. Rerun your reproduce script and confirm that the error is fixed!
+5. Think about edgecases and make sure your fix handles them as well
+Your thinking should be thorough and so it's fine if it's very long.
+""".strip()
+
+
+# Tool definitions
+tools: list[ChatCompletionToolParam] = [
+    {
+        "type": "function",
+        "function": {
+            "name": "bash",
+            "description": "Run a bash command in the sandbox and get the output",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "description": "The bash command to execute",
+                    }
+                },
+                "required": ["command"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "str_replace_editor",
+            "description": "Multi-function text editor for viewing and editing files",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "enum": [
+                            "view",
+                            "create",
+                            "str_replace",
+                            "insert",
+                            "undo_edit",
+                        ],
+                        "description": "The editor command to execute",
+                    },
+                    "path": {
+                        "type": "string",
+                        "description": "Path to the file",
+                    },
+                    "view_range": {
+                        "type": "array",
+                        "items": {"type": "integer"},
+                        "description": "Line range to view [start, end] (1-indexed, inclusive)",
+                    },
+                    "old_str": {
+                        "type": "string",
+                        "description": "String to replace (for str_replace command)",
+                    },
+                    "new_str": {
+                        "type": "string",
+                        "description": "New string to insert (for str_replace and create commands)",
+                    },
+                    "insert_line": {
+                        "type": "integer",
+                        "description": "Line number to insert at (for insert command)",
+                    },
+                },
+                "required": ["command", "path"],
+            },
+        },
+    },
+]
+
+
+async def art_style_rollout(
+    model: art.Model[ARTModelConfig],
+    instance: Instance,
+    sandbox: Sandbox,
+    reward_power: float = 1.0,
+) -> art.Trajectory:
+    """
+    Execute an ART-style rollout for solving a SWE-bench instance.
+
+    Args:
+        model: The ART model to use for generation
+        instance: The SWE-bench instance to solve
+        sandbox: The sandbox environment for code execution
+        reward_power: Power to apply to progress metric in reward calculation
+
+    Returns:
+        art.Trajectory with messages, choices, reward, and metrics
+    """
+    # Initialize trajectory
+    trajectory = art.Trajectory(
+        messages_and_choices=[
+            {
+                "role": "system",
+                "content": model.config.system_prompt,
+            },
+            {
+                "role": "user",
+                "content": model.config.instance_prompt_template.format(
+                    problem_statement=instance["problem_statement"],
+                    working_dir="/testbed",
+                ),
+            },
+        ],
+        tools=tools,
+        reward=0.0,
+        metrics={
+            "resolved": False,
+            "progress": 0.0,
+            "maintenance": 0.0,
+            "regression": 0.0,
+            "steps_taken": 0,
+        },
+        metadata={
+            "instance_id": instance["instance_id"],
+        },
+    )
+
+    # Get OpenAI client
+    client = model.openai_client()
+
+    # Main interaction loop
+    for step in range(model.config.max_steps):
+        trajectory.logs.append(f"Step {step + 1}/{model.config.max_steps}")
+
+        try:
+            # Get model completion
+            response = await client.chat.completions.create(
+                model=model.get_inference_name(),
+                messages=trajectory.messages(),
+                tools=tools,
+                tool_choice="auto",
+                temperature=model.config.temperature,
+            )
+        except Exception as e:
+            error_msg = f"Error getting completion: {type(e).__name__}: {str(e)}"
+            trajectory.logs.append(error_msg)
+            print(error_msg)
+            break
+
+        # Extract assistant message
+        assistant_message = response.choices[0].message
+
+        # Store the choice in trajectory
+        trajectory.messages_and_choices.append(response.choices[0])
+
+        # Log assistant response
+        if assistant_message.content:
+            trajectory.logs.append(f"Assistant: {assistant_message.content[:200]}...")
+
+        # Check if we're done (no tool calls)
+        if not assistant_message.tool_calls:
+            trajectory.logs.append("No tool calls - completing rollout")
+            trajectory.metrics["steps_taken"] = step + 1
+            break
+
+        # Execute tool calls
+        for tool_call in assistant_message.tool_calls:
+            tool_name = tool_call.function.name
+
+            # Parse tool arguments
+            try:
+                tool_args = json.loads(tool_call.function.arguments)
+            except json.JSONDecodeError as e:
+                error_msg = f"Invalid JSON in tool arguments: {str(e)}"
+                trajectory.logs.append(f"Error: {error_msg}")
+                print(f"Error: {error_msg}")
+
+                error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                trajectory.messages_and_choices.append(error_response)
+                continue
+
+            trajectory.logs.append(
+                f"Executing {tool_name}: {json.dumps(tool_args, indent=2)}"
+            )
+
+            # Execute the tool
+            try:
+                if tool_name == "bash":
+                    command = tool_args.get("command", "")
+                    exit_code, output = await sandbox.exec(command, timeout=60)
+
+                    # Truncate output if needed
+                    if len(output) > model.config.max_output_length:
+                        output = (
+                            output[: model.config.max_output_length // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-model.config.max_output_length // 2 :]
+                        )
+
+                    tool_result = f"Exit code: {exit_code}\nOutput:\n{output}"
+
+                elif tool_name == "str_replace_editor":
+                    output = await sandbox.edit(
+                        command=tool_args["command"],
+                        path=tool_args.get("path"),
+                        file_text=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] == "create"
+                            else None
+                        ),
+                        view_range=tool_args.get("view_range"),
+                        old_str=tool_args.get("old_str"),
+                        new_str=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] != "create"
+                            else None
+                        ),
+                        insert_line=tool_args.get("insert_line"),
+                    )
+
+                    # Truncate output if needed
+                    if len(output) > model.config.max_output_length:
+                        output = (
+                            output[: model.config.max_output_length // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-model.config.max_output_length // 2 :]
+                        )
+
+                    tool_result = output
+
+                else:
+                    tool_result = f"Unknown tool: {tool_name}"
+
+                trajectory.logs.append(f"Tool result: {tool_result[:200]}...")
+
+                # Add tool response to messages
+                tool_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": tool_result,
+                }
+                trajectory.messages_and_choices.append(tool_response)
+
+            except Exception as e:
+                error_msg = f"Error executing {tool_name}: {type(e).__name__}: {str(e)}"
+                trajectory.logs.append(f"Error: {error_msg}")
+                print(f"Error: {error_msg}")
+
+                exec_error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                trajectory.messages_and_choices.append(exec_error_response)
+
+    # Run final evaluation
+    trajectory.logs.append("Running final evaluation...")
+
+    try:
+        # Run FAIL_TO_PASS tests
+        failed_f2p, passed_f2p = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"],
+            timeout=120 + int(len(instance["FAIL_TO_PASS"]) * 0.05),
+        )
+
+        # Run PASS_TO_PASS tests
+        failed_p2p, passed_p2p = await sandbox.run_tests(
+            instance["PASS_TO_PASS"],
+            timeout=120 + int(len(instance["PASS_TO_PASS"]) * 0.05),
+        )
+
+        # Calculate metrics
+        total_f2p = len(instance["FAIL_TO_PASS"])
+        total_p2p = len(instance["PASS_TO_PASS"])
+
+        progress = passed_f2p / total_f2p if total_f2p > 0 else 0.0
+        failure = failed_f2p / total_f2p if total_f2p > 0 else 0.0
+        maintenance = passed_p2p / total_p2p if total_p2p > 0 else 1.0
+        regression = failed_p2p / total_p2p if total_p2p > 0 else 0.0
+
+        # Reconcile metrics pessimistically
+        progress = min(progress, 1 - failure)
+        maintenance = min(maintenance, 1 - regression)
+
+        # Check if resolved
+        resolved = (
+            failed_f2p == 0
+            and passed_f2p == total_f2p
+            and failed_p2p == 0
+            and passed_p2p == total_p2p
+        )
+
+        # Calculate reward
+        trajectory.reward = (
+            0.2 * maintenance + 0.3 * (progress**reward_power) + 0.5 * float(resolved)
+        )
+
+        # Update metrics
+        trajectory.metrics.update(
+            {
+                "progress": progress,
+                "maintenance": maintenance,
+                "regression": regression,
+                "resolved": resolved,
+                "failed_f2p": failed_f2p,
+                "passed_f2p": passed_f2p,
+                "failed_p2p": failed_p2p,
+                "passed_p2p": passed_p2p,
+            }
+        )
+
+        trajectory.logs.append(
+            f"Evaluation complete - Progress: {progress:.2f}, "
+            f"Maintenance: {maintenance:.2f}, Resolved: {resolved}"
+        )
+
+    except Exception as e:
+        error_msg = f"Error during evaluation: {type(e).__name__}: {str(e)}"
+        trajectory.logs.append(error_msg)
+        print(error_msg)
+        traceback.print_exc()
+
+    # DEBUG
+    # Serialize trajectory to file
+    import os
+
+    trajectory_path = f"./trajectories/{instance['instance_id']}.json"
+    os.makedirs(os.path.dirname(trajectory_path), exist_ok=True)
+    with open(trajectory_path, "w") as f:
+        f.write(trajectory.model_dump_json(indent=2))
+    trajectory.logs.append(f"Trajectory saved to {trajectory_path}")
+
+    return trajectory
diff --git a/dev/swebench/claude_rollout.py b/dev/swebench/claude_rollout.py
new file mode 100644
index 00000000..a580e3c8
--- /dev/null
+++ b/dev/swebench/claude_rollout.py
@@ -0,0 +1,341 @@
+import asyncio
+import json
+import os
+import traceback
+
+import openai
+from dotenv import load_dotenv
+from instances import as_instances_iter, get_filtered_swe_smith_instances_df
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessageParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionToolParam,
+)
+from openai.types.chat.chat_completion_message_tool_call_param import (
+    ChatCompletionMessageToolCallParam,
+)
+from sandbox import Sandbox, new_sandbox
+
+load_dotenv()
+
+instance_prompt = """
+<uploaded_files>
+{{working_dir}}
+</uploaded_files>
+I've uploaded a python code repository in the directory {{working_dir}}. Consider the following PR description:
+
+<pr_description>
+{{problem_statement}}
+</pr_description>
+
+Can you help me implement the necessary changes to the repository so that the requirements specified in the <pr_description> are met?
+I've already taken care of all changes to any of the test files described in the <pr_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!
+Your task is to make the minimal changes to non-tests files in the {{working_dir}} directory to ensure the <pr_description> is satisfied.
+Follow these steps to resolve the issue:
+1. As a first step, it might be a good idea to find and read code relevant to the <pr_description>
+2. Create a script to reproduce the error and execute it with `python <filename.py>` using the bash tool, to confirm the error
+3. Edit the sourcecode of the repo to resolve the issue
+4. Rerun your reproduce script and confirm that the error is fixed!
+5. Think about edgecases and make sure your fix handles them as well
+Your thinking should be thorough and so it's fine if it's very long.
+""".strip()
+
+tools: list[ChatCompletionToolParam] = [
+    {
+        "type": "function",
+        "function": {
+            "name": "bash",
+            "description": "Run a bash command in the sandbox and get the output",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "description": "The bash command to execute",
+                    }
+                },
+                "required": ["command"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "str_replace_editor",
+            "description": "Multi-function text editor for viewing and editing files",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "enum": [
+                            "view",
+                            "create",
+                            "str_replace",
+                            "insert",
+                            "undo_edit",
+                        ],
+                        "description": "The editor command to execute",
+                    },
+                    "path": {
+                        "type": "string",
+                        "description": "Path to the file",
+                    },
+                    "view_range": {
+                        "type": "array",
+                        "items": {"type": "integer"},
+                        "description": "Line range to view [start, end] (1-indexed, inclusive)",
+                    },
+                    "old_str": {
+                        "type": "string",
+                        "description": "String to replace (for str_replace command)",
+                    },
+                    "new_str": {
+                        "type": "string",
+                        "description": "New string to insert (for str_replace and create commands)",
+                    },
+                    "insert_line": {
+                        "type": "integer",
+                        "description": "Line number to insert at (for insert command)",
+                    },
+                },
+                "required": ["command", "path"],
+            },
+        },
+    },
+]
+
+MAX_OUTPUT_LENGTH = 1000
+
+
+async def claude_rollout(
+    client: openai.AsyncOpenAI,
+    problem_statement: str,
+    sandbox: Sandbox,
+    max_steps: int = 30,
+) -> str:
+    """
+    Roll out a Claude agent on a given problem statement in an isolated sandbox.
+
+    Args:
+        problem_statement: The problem statement to solve.
+        sandbox: The sandbox to use for the rollout.
+        max_steps: Maximum number of steps before terminating.
+
+    Returns:
+        The contents of the assistant's final response.
+    """
+    messages: list[ChatCompletionMessageParam] = [
+        {
+            "role": "user",
+            "content": instance_prompt.format(
+                problem_statement=problem_statement, working_dir="/testbed"
+            ),
+        },
+    ]
+    for step in range(max_steps):
+        print(f"\n[Step {step + 1}/{max_steps}]")
+        try:
+            response = await client.chat.completions.create(
+                model="anthropic/claude-sonnet-4",
+                messages=messages,
+                tools=tools,
+                tool_choice="auto",
+                max_tokens=4096,
+            )
+        except Exception as e:
+            print(f"Error getting completion: {type(e).__name__}: {str(e)}")
+            raise
+        assistant_message = response.choices[0].message
+        assistant_msg_dict: ChatCompletionAssistantMessageParam = {
+            "role": "assistant",
+            "content": assistant_message.content,
+        }
+        if assistant_message.tool_calls:
+            tool_calls: list[ChatCompletionMessageToolCallParam] = []
+            for tool_call in assistant_message.tool_calls:
+                tool_calls.append(
+                    {
+                        "id": tool_call.id,
+                        "type": "function",
+                        "function": {
+                            "name": tool_call.function.name,
+                            "arguments": tool_call.function.arguments,
+                        },
+                    }
+                )
+            assistant_msg_dict["tool_calls"] = tool_calls
+        messages.append(assistant_msg_dict)
+        if assistant_message.content:
+            print(f"Assistant: {assistant_message.content[:200]}...")
+        if not assistant_message.tool_calls:
+            print("No tool calls - completing rollout")
+            return assistant_message.content or "No response generated"
+        for tool_call in assistant_message.tool_calls:
+            tool_name = tool_call.function.name
+            try:
+                tool_args = json.loads(tool_call.function.arguments)
+            except json.JSONDecodeError as e:
+                error_msg = f"Invalid JSON in tool arguments: {str(e)}"
+                print(f"Error: {error_msg}")
+                error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                messages.append(error_response)
+                continue
+            print(f"Executing {tool_name}: {json.dumps(tool_args, indent=2)}")
+            try:
+                if tool_name == "bash":
+                    command = tool_args.get("command", "")
+                    exit_code, output = await sandbox.exec(command, timeout=60)
+                    if len(output) > MAX_OUTPUT_LENGTH:
+                        output = (
+                            output[: MAX_OUTPUT_LENGTH // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-MAX_OUTPUT_LENGTH // 2 :]
+                        )
+                    tool_result = f"Exit code: {exit_code}\nOutput:\n{output}"
+                elif tool_name == "str_replace_editor":
+                    output = await sandbox.edit(
+                        command=tool_args["command"],
+                        path=tool_args.get("path"),
+                        file_text=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] == "create"
+                            else None
+                        ),
+                        view_range=tool_args.get("view_range"),
+                        old_str=tool_args.get("old_str"),
+                        new_str=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] != "create"
+                            else None
+                        ),
+                        insert_line=tool_args.get("insert_line"),
+                    )
+                    if len(output) > MAX_OUTPUT_LENGTH:
+                        output = (
+                            output[: MAX_OUTPUT_LENGTH // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-MAX_OUTPUT_LENGTH // 2 :]
+                        )
+                    tool_result = output
+                else:
+                    tool_result = f"Unknown tool: {tool_name}"
+                print(f"Tool result: {tool_result[:200]}...")
+                tool_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": tool_result,
+                }
+                messages.append(tool_response)
+            except Exception as e:
+                error_msg = f"Error executing {tool_name}: {type(e).__name__}: {str(e)}"
+                print(f"Error: {error_msg}")
+                exec_error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                messages.append(exec_error_response)
+    print(f"\nExhausted {max_steps} steps without completion")
+    raise Exception(f"Agent did not complete task within {max_steps} steps")
+
+
+async def test_claude_rollout(instance_idx: int) -> None:
+    client = openai.AsyncOpenAI(
+        api_key=os.getenv("OPENROUTER_API_KEY"), base_url="https://openrouter.ai/api/v1"
+    )
+
+    instance = next(
+        get_filtered_swe_smith_instances_df()
+        .pipe(lambda df: df.tail(-instance_idx) if instance_idx > 0 else df)
+        .pipe(as_instances_iter)
+    )
+
+    print(f"\n{'=' * 60}")
+    print(f"Testing instance: {instance.get('instance_id', 'Unknown')}")
+    print(f"Repository: {instance.get('repo', 'Unknown')}")
+    print(f"{'=' * 60}\n")
+
+    # Calculate dynamic timeout based on number of tests
+    # Formula: base_timeout + num_tests * per_test_time
+    base_timeout = 120  # Base time for dependency installation
+    per_test_time = 0.05  # Per-test time (reduced since most tests are fast)
+
+    fail_to_pass_timeout = int(
+        base_timeout + len(instance["FAIL_TO_PASS"]) * per_test_time
+    )
+    pass_to_pass_timeout = int(
+        base_timeout + len(instance["PASS_TO_PASS"]) * per_test_time
+    )
+
+    async with new_sandbox(image=instance["image_name"], provider="daytona") as sandbox:
+        print("1. Running initial FAIL_TO_PASS tests (should all pass)...")
+        failed, passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        assert failed == 0
+        assert passed == len(instance["FAIL_TO_PASS"])
+        print(f"   ✓ All {passed} tests passed\n")
+
+        print("2. Applying patch to break tests...")
+        await sandbox.apply_patch(instance["patch"], 10)
+
+        print("3. Running FAIL_TO_PASS tests after patch (should all fail)...")
+        failed, passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        assert failed == len(instance["FAIL_TO_PASS"])
+        assert passed == 0
+        print(f"   ✓ All {failed} tests now failing as expected\n")
+
+        print("4. Running PASS_TO_PASS tests (should all pass)...")
+        failed, passed = await sandbox.run_tests(
+            instance["PASS_TO_PASS"], pass_to_pass_timeout
+        )
+        assert failed == 0
+        assert passed == len(instance["PASS_TO_PASS"])
+        print(f"   ✓ All {passed} tests passed\n")
+
+        print("5. Running Claude rollout to fix the issue...")
+        print(f"\nProblem statement:\n{instance['problem_statement'][:500]}...\n")
+
+        try:
+            response = await claude_rollout(
+                client, instance["problem_statement"], sandbox
+            )
+            print(f"\nClaude's final response: {response[:200]}...")
+        except Exception as e:
+            print(f"\nClaude rollout failed: {type(e).__name__}: {str(e)}")
+            traceback.print_exc()
+
+        print("\n6. Running final tests...")
+        f2p_failed, f2p_passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        p2p_failed, p2p_passed = await sandbox.run_tests(
+            instance["PASS_TO_PASS"], pass_to_pass_timeout
+        )
+
+        print("\nFinal results:")
+        print(
+            f"FAIL_TO_PASS: {f2p_passed} passed, {f2p_failed} failed (out of {len(instance['FAIL_TO_PASS'])})"
+        )
+        print(
+            f"PASS_TO_PASS: {p2p_passed} passed, {p2p_failed} failed (out of {len(instance['PASS_TO_PASS'])})"
+        )
+
+        if f2p_failed == 0 and p2p_failed == 0:
+            print("\n✅ SUCCESS: All tests passing!")
+        else:
+            print("\n❌ FAILURE: Some tests still failing")
+
+    await client.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_claude_rollout(instance_idx=0))
diff --git a/dev/swebench/instance_filter.py b/dev/swebench/instance_filter.py
new file mode 100644
index 00000000..a13a4101
--- /dev/null
+++ b/dev/swebench/instance_filter.py
@@ -0,0 +1,226 @@
+"""
+Filter SWE-bench instances based on test results to identify high-quality training instances.
+
+Filters instances that meet all of the following criteria:
+1. f2p_initial.passed == len(f2p) and f2p_initial.failed == 0
+2. f2p_post_patch.failed == len(f2p) and f2p_post_patch.passed == 0
+3. p2p.failed == 0 and p2p.passed == len(p2p)
+"""
+
+import json
+from collections import defaultdict
+from typing import Dict, Set
+
+import polars as pl
+
+
+def load_test_results(
+    results_file: str = "analysis/instance_test_results.jsonl",
+) -> Dict[str, dict]:
+    """
+    Load test results from JSONL file and return the most optimistic result for each instance.
+
+    Args:
+        results_file: Path to the test results JSONL file
+
+    Returns:
+        Dictionary mapping instance_id to test results
+    """
+    results_by_instance = defaultdict(list)
+
+    # Load all results
+    with open(results_file, "r") as f:
+        for line in f:
+            if line.strip():
+                try:
+                    result = json.loads(line)
+                    results_by_instance[result["instance_id"]].append(result)
+                except json.JSONDecodeError:
+                    continue
+
+    # Select most optimistic result for each instance
+    optimistic_results = {}
+    for instance_id, results in results_by_instance.items():
+        # Filter out results with errors
+        valid_results = [r for r in results if "error" not in r or not r["error"]]
+
+        if not valid_results:
+            continue
+
+        # Select the result where tests pass best
+        best_result = max(
+            valid_results,
+            key=lambda r: (
+                (r.get("f2p_initial", {}).get("passed", 0) or 0)
+                + (r.get("p2p", {}).get("passed", 0) or 0)
+                - (r.get("f2p_post_patch", {}).get("passed", 0) or 0)
+            ),
+        )
+
+        optimistic_results[instance_id] = best_result
+
+    return optimistic_results
+
+
+def check_instance_quality(result: dict, instance: dict) -> bool:
+    """
+    Check if an instance meets the quality criteria.
+
+    Args:
+        result: Test result from instance_test_results.jsonl
+        instance: Instance data with FAIL_TO_PASS and PASS_TO_PASS test lists
+
+    Returns:
+        True if instance meets all quality criteria
+    """
+    # Check for error
+    if "error" in result and result["error"]:
+        return False
+
+    # Get test counts
+    f2p_count = len(instance.get("FAIL_TO_PASS", []))
+    p2p_count = len(instance.get("PASS_TO_PASS", []))
+
+    # Get test results
+    f2p_initial = result.get("f2p_initial", {})
+    f2p_post_patch = result.get("f2p_post_patch", {})
+    p2p = result.get("p2p", {})
+
+    # Check criterion 1: f2p_initial.passed == len(f2p) and f2p_initial.failed == 0
+    if f2p_initial.get("passed") != f2p_count or f2p_initial.get("failed") != 0:
+        return False
+
+    # Check criterion 2: f2p_post_patch.failed == len(f2p) and f2p_post_patch.passed == 0
+    if f2p_post_patch.get("failed") != f2p_count or f2p_post_patch.get("passed") != 0:
+        return False
+
+    # Check criterion 3: p2p.failed == 0 and p2p.passed == len(p2p)
+    if p2p.get("failed") != 0 or p2p.get("passed") != p2p_count:
+        return False
+
+    return True
+
+
+def get_quality_instance_ids(
+    instances_df: pl.DataFrame,
+    results_file: str = "analysis/instance_test_results.jsonl",
+    require_non_zero_tests: bool = True,
+) -> Set[str]:
+    """
+    Get set of instance IDs that meet quality criteria.
+
+    Args:
+        instances_df: DataFrame of SWE-bench instances
+        results_file: Path to test results file
+        require_non_zero_tests: If True, exclude instances with zero tests
+
+    Returns:
+        Set of instance IDs that meet quality criteria
+    """
+    # Load test results
+    test_results = load_test_results(results_file)
+
+    # Check each instance
+    quality_instances = set()
+
+    for instance in instances_df.iter_rows(named=True):
+        instance_id = instance["instance_id"]
+
+        # Skip if no test results
+        if instance_id not in test_results:
+            continue
+
+        # Skip if requiring non-zero tests and instance has none
+        if require_non_zero_tests:
+            f2p_count = len(instance.get("FAIL_TO_PASS", []))
+            p2p_count = len(instance.get("PASS_TO_PASS", []))
+            if f2p_count == 0 and p2p_count == 0:
+                continue
+
+        # Check quality criteria
+        if check_instance_quality(test_results[instance_id], instance):
+            quality_instances.add(instance_id)
+
+    return quality_instances
+
+
+def filter_quality_instances(
+    instances_df: pl.DataFrame,
+    results_file: str = "analysis/instance_test_results.jsonl",
+    require_non_zero_tests: bool = True,
+) -> pl.DataFrame:
+    """
+    Filter instances DataFrame to only include quality instances.
+
+    Args:
+        instances_df: DataFrame of SWE-bench instances
+        results_file: Path to test results file
+        require_non_zero_tests: If True, exclude instances with zero tests
+
+    Returns:
+        Filtered DataFrame with only quality instances
+    """
+    quality_ids = get_quality_instance_ids(
+        instances_df, results_file, require_non_zero_tests
+    )
+    return instances_df.filter(pl.col("instance_id").is_in(list(quality_ids)))
+
+
+def save_quality_instance_list(
+    instances_df: pl.DataFrame,
+    output_file: str = "quality_instances.txt",
+    results_file: str = "analysis/instance_test_results.jsonl",
+    require_non_zero_tests: bool = True,
+) -> int:
+    """
+    Save list of quality instance IDs to a text file.
+
+    Args:
+        instances_df: DataFrame of SWE-bench instances
+        output_file: Path to output file
+        results_file: Path to test results file
+        require_non_zero_tests: If True, exclude instances with zero tests
+
+    Returns:
+        Number of quality instances found
+    """
+    quality_ids = get_quality_instance_ids(
+        instances_df, results_file, require_non_zero_tests
+    )
+
+    with open(output_file, "w") as f:
+        for instance_id in sorted(quality_ids):
+            f.write(f"{instance_id}\n")
+
+    return len(quality_ids)
+
+
+if __name__ == "__main__":
+    # Example usage
+    from instances import get_filtered_swe_smith_instances_df
+
+    print("Loading instances...")
+    instances_df = get_filtered_swe_smith_instances_df()
+
+    print("Finding quality instances...")
+    quality_instances = filter_quality_instances(instances_df)
+
+    print(
+        f"Found {len(quality_instances)} quality instances out of {len(instances_df)} total"
+    )
+
+    # Save the list
+    count = save_quality_instance_list(instances_df, "quality_instances.txt")
+    print(f"Saved {count} quality instance IDs to quality_instances.txt")
+
+    # Show some statistics
+    print("\nQuality instance statistics:")
+    print(f"- Total instances: {len(instances_df)}")
+    print(f"- Quality instances: {len(quality_instances)}")
+    print(f"- Percentage: {len(quality_instances) / len(instances_df) * 100:.1f}%")
+
+    # Sample a few
+    if len(quality_instances) > 0:
+        print("\nSample quality instances:")
+        for _, instance in quality_instances.head(5).iterrows():
+            print(f"- {instance['instance_id']} ({instance['repo']})")
diff --git a/dev/swebench/pyproject.toml b/dev/swebench/pyproject.toml
index cc4a56f7..f047ede3 100644
--- a/dev/swebench/pyproject.toml
+++ b/dev/swebench/pyproject.toml
@@ -7,9 +7,10 @@ dependencies = [
     "daytona-sdk>=0.21.5",
     "langfuse>=2.60.7",
     "modal>=1.0.1",
-    "openpipe-art",
+    "openpipe-art[backend,plotting]",
     "sweagent",
     "swebench>=4.0.3",
+    "tenacity>=9.1.2",
 ]
 
 [tool.uv.sources]
diff --git a/dev/swebench/qwen_rollout.py b/dev/swebench/qwen_rollout.py
new file mode 100644
index 00000000..3812f8e7
--- /dev/null
+++ b/dev/swebench/qwen_rollout.py
@@ -0,0 +1,347 @@
+import asyncio
+import json
+import traceback
+
+import openai
+from dotenv import load_dotenv
+from instances import as_instances_iter, get_filtered_swe_smith_instances_df
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessageParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionToolParam,
+)
+from openai.types.chat.chat_completion_message_tool_call_param import (
+    ChatCompletionMessageToolCallParam,
+)
+from sandbox import Sandbox, new_sandbox
+
+load_dotenv()
+
+instance_prompt = """
+<uploaded_files>
+{working_dir}
+</uploaded_files>
+I've uploaded a python code repository in the directory {working_dir}. Consider the following PR description:
+
+<pr_description>
+{problem_statement}
+</pr_description>
+
+Can you help me implement the necessary changes to the repository so that the requirements specified in the <pr_description> are met?
+I've already taken care of all changes to any of the test files described in the <pr_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!
+Your task is to make the minimal changes to non-tests files in the {working_dir} directory to ensure the <pr_description> is satisfied.
+Follow these steps to resolve the issue:
+1. As a first step, it might be a good idea to find and read code relevant to the <pr_description>
+2. Create a script to reproduce the error and execute it with `python <filename.py>` using the bash tool, to confirm the error
+3. Edit the sourcecode of the repo to resolve the issue
+4. Rerun your reproduce script and confirm that the error is fixed!
+5. Think about edgecases and make sure your fix handles them as well
+Your thinking should be thorough and so it's fine if it's very long.
+""".strip()
+
+tools: list[ChatCompletionToolParam] = [
+    {
+        "type": "function",
+        "function": {
+            "name": "bash",
+            "description": "Run a bash command in the sandbox and get the output",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "description": "The bash command to execute",
+                    }
+                },
+                "required": ["command"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "str_replace_editor",
+            "description": "Multi-function text editor for viewing and editing files",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "command": {
+                        "type": "string",
+                        "enum": [
+                            "view",
+                            "create",
+                            "str_replace",
+                            "insert",
+                            "undo_edit",
+                        ],
+                        "description": "The editor command to execute",
+                    },
+                    "path": {
+                        "type": "string",
+                        "description": "Path to the file",
+                    },
+                    "view_range": {
+                        "type": "array",
+                        "items": {"type": "integer"},
+                        "description": "Line range to view [start, end] (1-indexed, inclusive)",
+                    },
+                    "old_str": {
+                        "type": "string",
+                        "description": "String to replace (for str_replace command)",
+                    },
+                    "new_str": {
+                        "type": "string",
+                        "description": "New string to insert (for str_replace and create commands)",
+                    },
+                    "insert_line": {
+                        "type": "integer",
+                        "description": "Line number to insert at (for insert command)",
+                    },
+                },
+                "required": ["command", "path"],
+            },
+        },
+    },
+]
+
+MAX_OUTPUT_LENGTH = 1000
+
+
+async def qwen_rollout(
+    client: openai.AsyncOpenAI,
+    problem_statement: str,
+    sandbox: Sandbox,
+    max_steps: int = 30,
+) -> str:
+    """
+    Roll out a Qwen agent on a given problem statement in an isolated sandbox.
+
+    Args:
+        problem_statement: The problem statement to solve.
+        sandbox: The sandbox to use for the rollout.
+        max_steps: Maximum number of steps before terminating.
+
+    Returns:
+        The contents of the assistant's final response.
+    """
+    messages: list[ChatCompletionMessageParam] = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant that can interact with a computer to solve tasks.",
+        },
+        {
+            "role": "user",
+            "content": instance_prompt.format(
+                problem_statement=problem_statement, working_dir="/testbed"
+            ),
+        },
+    ]
+    for step in range(max_steps):
+        print(f"\n[Step {step + 1}/{max_steps}]")
+        try:
+            response = await client.chat.completions.create(
+                model="willcb/Qwen3-32B",
+                messages=messages,
+                tools=tools,
+                tool_choice="auto",
+                max_tokens=4096,
+            )
+        except Exception as e:
+            print(f"Error getting completion: {type(e).__name__}: {str(e)}")
+            raise
+        assistant_message = response.choices[0].message
+        assistant_msg_dict: ChatCompletionAssistantMessageParam = {
+            "role": "assistant",
+            "content": assistant_message.content,
+        }
+        if assistant_message.tool_calls:
+            tool_calls: list[ChatCompletionMessageToolCallParam] = []
+            for tool_call in assistant_message.tool_calls:
+                tool_calls.append(
+                    {
+                        "id": tool_call.id,
+                        "type": "function",
+                        "function": {
+                            "name": tool_call.function.name,
+                            "arguments": tool_call.function.arguments,
+                        },
+                    }
+                )
+            assistant_msg_dict["tool_calls"] = tool_calls
+        messages.append(assistant_msg_dict)
+        if assistant_message.content:
+            print(f"Assistant: {assistant_message.content[:200]}...")
+        if not assistant_message.tool_calls:
+            print("No tool calls - completing rollout")
+            return assistant_message.content or "No response generated"
+        for tool_call in assistant_message.tool_calls:
+            tool_name = tool_call.function.name
+            try:
+                tool_args = json.loads(tool_call.function.arguments)
+            except json.JSONDecodeError as e:
+                error_msg = f"Invalid JSON in tool arguments: {str(e)}"
+                print(f"Error: {error_msg}")
+                error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                messages.append(error_response)
+                continue
+            print(f"Executing {tool_name}: {json.dumps(tool_args, indent=2)}")
+            try:
+                if tool_name == "bash":
+                    command = tool_args.get("command", "")
+                    exit_code, output = await sandbox.exec(command, timeout=60)
+                    if len(output) > MAX_OUTPUT_LENGTH:
+                        output = (
+                            output[: MAX_OUTPUT_LENGTH // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-MAX_OUTPUT_LENGTH // 2 :]
+                        )
+                    tool_result = f"Exit code: {exit_code}\nOutput:\n{output}"
+                elif tool_name == "str_replace_editor":
+                    output = await sandbox.edit(
+                        command=tool_args["command"],
+                        path=tool_args.get("path"),
+                        file_text=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] == "create"
+                            else None
+                        ),
+                        view_range=tool_args.get("view_range"),
+                        old_str=tool_args.get("old_str"),
+                        new_str=(
+                            tool_args.get("new_str")
+                            if tool_args["command"] != "create"
+                            else None
+                        ),
+                        insert_line=tool_args.get("insert_line"),
+                    )
+                    if len(output) > MAX_OUTPUT_LENGTH:
+                        output = (
+                            output[: MAX_OUTPUT_LENGTH // 2]
+                            + "\n\n[... truncated ...]\n\n"
+                            + output[-MAX_OUTPUT_LENGTH // 2 :]
+                        )
+                    tool_result = output
+                else:
+                    tool_result = f"Unknown tool: {tool_name}"
+                print(f"Tool result: {tool_result[:200]}...")
+                tool_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": tool_result,
+                }
+                messages.append(tool_response)
+            except Exception as e:
+                error_msg = f"Error executing {tool_name}: {type(e).__name__}: {str(e)}"
+                print(f"Error: {error_msg}")
+                exec_error_response: ChatCompletionToolMessageParam = {
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": error_msg,
+                }
+                messages.append(exec_error_response)
+    print(f"\nExhausted {max_steps} steps without completion")
+    raise Exception(f"Agent did not complete task within {max_steps} steps")
+
+
+async def test_qwen_rollout(instance_idx: int) -> None:
+    client = openai.AsyncOpenAI(api_key="default", base_url="http://localhost:8000/v1")
+
+    instance = next(
+        get_filtered_swe_smith_instances_df()
+        .pipe(lambda df: df.tail(-instance_idx) if instance_idx > 0 else df)
+        .pipe(as_instances_iter)
+    )
+
+    print(f"\n{'=' * 60}")
+    print(f"Testing instance: {instance.get('instance_id', 'Unknown')}")
+    print(f"Repository: {instance.get('repo', 'Unknown')}")
+    print(f"{'=' * 60}\n")
+
+    # Calculate dynamic timeout based on number of tests
+    # Formula: base_timeout + num_tests * per_test_time
+    base_timeout = 120  # Base time for dependency installation
+    per_test_time = 0.05  # Per-test time (reduced since most tests are fast)
+
+    fail_to_pass_timeout = int(
+        base_timeout + len(instance["FAIL_TO_PASS"]) * per_test_time
+    )
+    pass_to_pass_timeout = int(
+        base_timeout + len(instance["PASS_TO_PASS"]) * per_test_time
+    )
+
+    async with new_sandbox(image=instance["image_name"], provider="daytona") as sandbox:
+        print("1. Running initial FAIL_TO_PASS tests (should all pass)...")
+        failed, passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        assert failed == 0
+        assert passed == len(instance["FAIL_TO_PASS"])
+        print(f"   ✓ All {passed} tests passed\n")
+
+        print("2. Applying patch to break tests...")
+        await sandbox.apply_patch(instance["patch"], 10)
+
+        print("3. Running FAIL_TO_PASS tests after patch (should all fail)...")
+        failed, passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        assert failed == len(instance["FAIL_TO_PASS"])
+        assert passed == 0
+        print(f"   ✓ All {failed} tests now failing as expected\n")
+
+        print("4. Running PASS_TO_PASS tests (should all pass)...")
+        failed, passed = await sandbox.run_tests(
+            instance["PASS_TO_PASS"], pass_to_pass_timeout
+        )
+        assert failed == 0
+        assert passed == len(instance["PASS_TO_PASS"])
+        print(f"   ✓ All {passed} tests passed\n")
+
+        print("5. Running Qwen rollout to fix the issue...")
+        print(f"\nProblem statement:\n{instance['problem_statement'][:500]}...\n")
+
+        try:
+            response = await qwen_rollout(
+                client, instance["problem_statement"], sandbox
+            )
+            print(f"\nQwen's final response: {response[:200]}...")
+        except Exception as e:
+            print(f"\nQwen rollout failed: {type(e).__name__}: {str(e)}")
+            traceback.print_exc()
+
+        print("\n6. Running final tests...")
+        f2p_failed, f2p_passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"], fail_to_pass_timeout
+        )
+        p2p_failed, p2p_passed = await sandbox.run_tests(
+            instance["PASS_TO_PASS"], pass_to_pass_timeout
+        )
+
+        print("\nFinal results:")
+        print(
+            f"FAIL_TO_PASS: {f2p_passed} passed, {f2p_failed} failed (out of {len(instance['FAIL_TO_PASS'])})"
+        )
+        print(
+            f"PASS_TO_PASS: {p2p_passed} passed, {p2p_failed} failed (out of {len(instance['PASS_TO_PASS'])})"
+        )
+
+        if (
+            f2p_failed == 0
+            and f2p_passed == len(instance["FAIL_TO_PASS"])
+            and p2p_failed == 0
+            and p2p_passed == len(instance["PASS_TO_PASS"])
+        ):
+            print("\n✅ SUCCESS: All tests passing!")
+        else:
+            print("\n❌ FAILURE: Some tests still failing")
+
+    await client.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_qwen_rollout(instance_idx=5))
diff --git a/dev/swebench/sandbox/new.py b/dev/swebench/sandbox/new.py
index 635c5284..c7e967f1 100644
--- a/dev/swebench/sandbox/new.py
+++ b/dev/swebench/sandbox/new.py
@@ -4,6 +4,12 @@
 
 import daytona_sdk
 import modal
+from tenacity import (
+    retry,
+    retry_if_exception_message,
+    stop_after_attempt,
+    wait_exponential,
+)
 
 from .daytona import DaytonaSandbox
 from .modal import ModalSandbox
@@ -16,6 +22,14 @@
 modal.enable_output()
 
 
+# Retry decorator for sandbox creation
+create_sandbox_retry = retry(
+    stop=stop_after_attempt(5),
+    wait=wait_exponential(multiplier=2, min=2, max=30),
+    retry=retry_if_exception_message(match="No available runners|timeout|504 Gateway"),
+)
+
+
 @asynccontextmanager
 async def new_sandbox(
     *, image: str, provider: Provider, timeout: int = 60
@@ -38,23 +52,29 @@ async def new_sandbox(
     """
     if provider == "daytona":
         global daytona
-        for _ in range(2):
+
+        @create_sandbox_retry
+        async def create_daytona_sandbox():
+            global daytona
             try:
-                sandbox = await daytona.create(
+                return await daytona.create(
                     daytona_sdk.CreateSandboxFromImageParams(image=image),
                     timeout=timeout,
                 )
-                break
             except daytona_sdk.DaytonaError as e:
                 if "Event loop is closed" in str(e):
                     await daytona.close()
                     daytona = daytona_sdk.AsyncDaytona()
-                    continue
                 raise
+
         try:
+            sandbox = await create_daytona_sandbox()
             yield DaytonaSandbox(sandbox)
         finally:
-            await sandbox.delete()
+            try:
+                await sandbox.delete()
+            except Exception:
+                pass
     else:
         global modal_app_task
         if modal_app_task is None:
diff --git a/dev/swebench/sandbox/sandbox.py b/dev/swebench/sandbox/sandbox.py
index c53b1258..f439717e 100644
--- a/dev/swebench/sandbox/sandbox.py
+++ b/dev/swebench/sandbox/sandbox.py
@@ -1,3 +1,4 @@
+import os
 from abc import ABC, abstractmethod
 from typing import List, Literal, Optional
 
@@ -193,7 +194,12 @@ async def edit(
         )
         if exit_code != 0:
             # Copy the tool and its dependencies into the sandbox
-            host_tool_path = "/home/brad/art/dev/swebench/tools/edit_anthropic/bin/str_replace_editor"
+
+            # Get the path relative to this file
+            current_dir = os.path.dirname(os.path.abspath(__file__))
+            host_tool_path = os.path.join(
+                current_dir, "../tools/edit_anthropic/bin/str_replace_editor"
+            )
 
             # Read the tool from host
             with open(host_tool_path, "r") as f:
diff --git a/dev/swebench/test_art_style.py b/dev/swebench/test_art_style.py
new file mode 100755
index 00000000..308ae08c
--- /dev/null
+++ b/dev/swebench/test_art_style.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+Test script for ART-style rollout implementation.
+Tests a single instance to verify everything is working.
+"""
+
+import asyncio
+import os
+
+from art_style_rollout import ARTModelConfig, art_style_rollout
+from dotenv import load_dotenv
+from instances import as_instances_iter, get_filtered_swe_smith_instances_df
+from sandbox import new_sandbox
+
+import art
+
+
+async def test_single_rollout():
+    """Test a single rollout with detailed logging."""
+    # Load environment
+    load_dotenv()
+
+    # Set up OpenAI client for local inference
+    os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "default")
+    os.environ["OPENAI_BASE_URL"] = "http://localhost:8000/v1"
+
+    # Initialize model
+    model = art.Model(
+        name="willcb/Qwen3-32B",  # Use the same model as qwen_rollout
+        project="swebench-art-test",
+        config=ARTModelConfig(),
+    )
+
+    # Get a single instance
+    instance = next(
+        get_filtered_swe_smith_instances_df()
+        .tail(-5)  # Skip first 5 instances
+        .pipe(as_instances_iter)
+    )
+
+    print(f"Testing with instance: {instance['instance_id']}")
+    print(f"Repository: {instance.get('repo', 'Unknown')}")
+    print(f"Problem statement preview: {instance['problem_statement'][:200]}...")
+    print("\n" + "=" * 60 + "\n")
+
+    # Run rollout with sandbox
+    async with new_sandbox(image=instance["image_name"], provider="daytona") as sandbox:
+        print("Sandbox created successfully")
+
+        # Apply patch to break tests
+        print("Applying patch to break tests...")
+        await sandbox.apply_patch(instance["patch"], timeout=10)
+
+        # Verify tests are broken
+        print("\nVerifying tests are broken after patch...")
+        failed, passed = await sandbox.run_tests(
+            instance["FAIL_TO_PASS"][:1],
+            timeout=60,  # Test just one for speed
+        )
+        print(f"FAIL_TO_PASS after patch: {failed} failed, {passed} passed")
+        assert failed > 0, "Tests should be failing after patch"
+
+        # Run the rollout
+        print("\nRunning ART-style rollout...")
+        trajectory = await art_style_rollout(
+            model=model,
+            instance=instance,
+            sandbox=sandbox,
+            reward_power=1.0,
+        )
+
+        # Print results
+        print("\n" + "=" * 60)
+        print("ROLLOUT RESULTS")
+        print("=" * 60)
+        print(f"Steps taken: {trajectory.metrics['steps_taken']}")
+        print(f"Reward: {trajectory.reward:.3f}")
+        print(f"Progress: {trajectory.metrics['progress']:.3f}")
+        print(f"Maintenance: {trajectory.metrics['maintenance']:.3f}")
+        print(f"Resolved: {trajectory.metrics['resolved']}")
+        print(
+            f"FAIL_TO_PASS: {trajectory.metrics['passed_f2p']}/{trajectory.metrics['passed_f2p'] + trajectory.metrics['failed_f2p']} passed"
+        )
+        print(
+            f"PASS_TO_PASS: {trajectory.metrics['passed_p2p']}/{trajectory.metrics['passed_p2p'] + trajectory.metrics['failed_p2p']} passed"
+        )
+
+        # Print some logs
+        print("\nTrajectory logs (last 10):")
+        for log in trajectory.logs[-10:]:
+            print(f"  {log}")
+
+        return trajectory
+
+
+async def test_trajectory_group():
+    """Test creating a trajectory group (multiple rollouts)."""
+    load_dotenv()
+
+    # Set up OpenAI client
+    os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "default")
+    os.environ["OPENAI_BASE_URL"] = "http://localhost:8000/v1"
+
+    # Initialize model
+    model = art.Model(
+        name="willcb/Qwen3-32B",
+        project="swebench-art-test",
+        config=ARTModelConfig(max_steps=5),  # Very limited for testing
+    )
+
+    # Get an instance
+    instance = next(
+        get_filtered_swe_smith_instances_df().tail(-10).pipe(as_instances_iter)
+    )
+
+    print(f"Testing trajectory group with instance: {instance['instance_id']}")
+
+    # Helper function for rollout with sandbox
+    async def rollout_helper():
+        async with new_sandbox(
+            image=instance["image_name"], provider="daytona"
+        ) as sandbox:
+            await sandbox.apply_patch(instance["patch"], timeout=10)
+            return await art_style_rollout(model, instance, sandbox)
+
+    # Create trajectory group
+    trajectory_group = await art.gather_trajectories(
+        [rollout_helper() for _ in range(2)]  # Just 2 rollouts for testing
+    )
+
+    print(f"\nCompleted {len(trajectory_group)} rollouts")
+    print(f"Rewards: {[t.reward for t in trajectory_group]}")
+    print(
+        f"Average reward: {sum(t.reward for t in trajectory_group) / len(trajectory_group):.3f}"
+    )
+
+    return trajectory_group
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) > 1 and sys.argv[1] == "group":
+        print("Testing trajectory group...")
+        asyncio.run(test_trajectory_group())
+    else:
+        print("Testing single rollout...")
+        asyncio.run(test_single_rollout())
diff --git a/dev/swebench/test_instance_filter.py b/dev/swebench/test_instance_filter.py
new file mode 100755
index 00000000..7df4d03d
--- /dev/null
+++ b/dev/swebench/test_instance_filter.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Test the instance filter functionality."""
+
+from instance_filter import (
+    filter_quality_instances,
+    get_quality_instance_ids,
+    load_test_results,
+    save_quality_instance_list,
+)
+from instances import get_filtered_swe_smith_instances_df
+
+
+def main():
+    print("Testing instance filter functionality...")
+
+    # Load test results
+    print("\n1. Loading test results...")
+    test_results = load_test_results()
+    print(f"   Loaded {len(test_results)} instance results")
+
+    # Sample a few results
+    print("\n   Sample results:")
+    for i, (instance_id, result) in enumerate(list(test_results.items())[:3]):
+        print(f"   - {instance_id}:")
+        if "error" in result and result["error"]:
+            print(f"     Error: {result['error'][:50]}...")
+        else:
+            f2p_init = result.get("f2p_initial", {})
+            f2p_post = result.get("f2p_post_patch", {})
+            p2p = result.get("p2p", {})
+            print(
+                f"     F2P initial: passed={f2p_init.get('passed')}, failed={f2p_init.get('failed')}"
+            )
+            print(
+                f"     F2P post-patch: passed={f2p_post.get('passed')}, failed={f2p_post.get('failed')}"
+            )
+            print(f"     P2P: passed={p2p.get('passed')}, failed={p2p.get('failed')}")
+
+    # Load instances
+    print("\n2. Loading SWE-bench instances...")
+    instances_df = get_filtered_swe_smith_instances_df()
+    print(f"   Loaded {len(instances_df)} instances")
+
+    # Get quality instances
+    print("\n3. Finding quality instances...")
+    quality_ids = get_quality_instance_ids(instances_df)
+    print(f"   Found {len(quality_ids)} quality instances")
+
+    # Filter with non-zero test requirement
+    quality_ids_nonzero = get_quality_instance_ids(
+        instances_df, require_non_zero_tests=True
+    )
+    print(f"   Found {len(quality_ids_nonzero)} quality instances with non-zero tests")
+
+    # Filter DataFrame
+    print("\n4. Filtering DataFrame...")
+    quality_df = filter_quality_instances(instances_df)
+    print(f"   Filtered DataFrame has {len(quality_df)} instances")
+
+    # Show some statistics
+    print("\n5. Quality instance statistics:")
+    print(f"   - Total instances: {len(instances_df)}")
+    print(f"   - Quality instances (all): {len(quality_ids)}")
+    print(f"   - Quality instances (non-zero tests): {len(quality_ids_nonzero)}")
+    print(f"   - Percentage: {len(quality_ids_nonzero) / len(instances_df) * 100:.1f}%")
+
+    # Sample some quality instances
+    if len(quality_df) > 0:
+        print("\n6. Sample quality instances:")
+        for instance in quality_df.head(5).iter_rows(named=True):
+            f2p_count = len(instance.get("FAIL_TO_PASS", []))
+            p2p_count = len(instance.get("PASS_TO_PASS", []))
+            print(f"   - {instance['instance_id']} ({instance['repo']})")
+            print(f"     F2P tests: {f2p_count}, P2P tests: {p2p_count}")
+
+    # Save list
+    print("\n7. Saving quality instance list...")
+    count = save_quality_instance_list(instances_df, "quality_instances.txt")
+    print(f"   Saved {count} instance IDs to quality_instances.txt")
+
+    # Show repository distribution
+    if len(quality_df) > 0:
+        print("\n8. Repository distribution (top 10):")
+        repo_counts = (
+            quality_df.group_by("repo").count().sort("count", descending=True).head(10)
+        )
+        for row in repo_counts.iter_rows(named=True):
+            print(f"   - {row['repo']}: {row['count']} instances")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/dev/swebench/train_art_style.py b/dev/swebench/train_art_style.py
new file mode 100755
index 00000000..44aebb08
--- /dev/null
+++ b/dev/swebench/train_art_style.py
@@ -0,0 +1,353 @@
+#!/usr/bin/env python3
+"""
+ART-style training script for SWE-bench using a simplified rollout function.
+Inspired by qwen_rollout.py but following ART idioms.
+"""
+
+import argparse
+import asyncio
+import os
+
+import torch
+from art_style_rollout import ARTModelConfig, art_style_rollout
+from dotenv import load_dotenv
+from instance_filter import filter_quality_instances
+from instances import Instance, as_instances_iter, get_filtered_swe_smith_instances_df
+from sandbox import new_sandbox
+
+import art
+
+
+def setup_environment():
+    """Set up environment variables and directories."""
+    load_dotenv()
+
+    # Create necessary directories
+    os.makedirs("replays", exist_ok=True)
+    os.makedirs("trajectories", exist_ok=True)
+
+    # Print environment info
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    if torch.cuda.is_available():
+        print(f"CUDA devices: {torch.cuda.device_count()}")
+
+
+async def rollout_with_sandbox(
+    model: art.Model[ARTModelConfig],
+    instance: Instance,
+    reward_power: float = 1.0,
+) -> art.Trajectory:
+    """Run a single rollout with sandbox management."""
+    async with new_sandbox(image=instance["image_name"], provider="daytona") as sandbox:
+        # Apply the patch to break tests
+        await sandbox.apply_patch(instance["patch"], timeout=10)
+
+        # Run the rollout
+        trajectory = await art_style_rollout(
+            model=model,
+            instance=instance,
+            sandbox=sandbox,
+            reward_power=reward_power,
+        )
+
+    return trajectory
+
+
+async def train_inference_model(
+    model_name: str = "willcb/Qwen3-32B",
+    api_base: str = "http://localhost:8000/v1",
+    num_instances: int = 10,
+    rollouts_per_instance: int = 4,
+    reward_power: float = 1.0,
+    use_quality_filter: bool = True,
+    require_non_zero_tests: bool = True,
+):
+    """Train using an inference-only model (no gradient updates)."""
+    # Initialize model
+    model = art.Model(
+        name=model_name,
+        project="swebench-art-style",
+        config=ARTModelConfig(),
+    )
+
+    # Set up OpenAI client if using local inference
+    if api_base:
+        os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "default")
+        os.environ["OPENAI_BASE_URL"] = api_base
+
+    # Load instances
+    all_instances_df = get_filtered_swe_smith_instances_df()
+
+    # Apply quality filter if requested
+    if use_quality_filter:
+        instances_df = filter_quality_instances(
+            all_instances_df, require_non_zero_tests=require_non_zero_tests
+        )
+        print(
+            f"Filtered to {len(instances_df)} quality instances from {len(all_instances_df)} total"
+        )
+    else:
+        instances_df = all_instances_df
+        print(f"Using all {len(instances_df)} instances (no quality filter)")
+
+    instances = list(
+        instances_df.sample(n=min(num_instances, len(instances_df))).pipe(
+            as_instances_iter
+        )
+    )
+
+    print(f"Loaded {len(instances)} instances")
+
+    # Collect trajectories
+    all_trajectories = []
+
+    for i, instance in enumerate(instances):
+        print(f"\n{'=' * 60}")
+        print(f"Instance {i + 1}/{len(instances)}: {instance['instance_id']}")
+        print(f"{'=' * 60}")
+
+        # Run multiple rollouts for this instance
+        trajectory_group = await art.gather_trajectories(
+            [
+                rollout_with_sandbox(model, instance, reward_power)
+                for _ in range(rollouts_per_instance)
+            ]
+        )
+
+        # Log results
+        rewards = [t.reward for t in trajectory_group]
+        resolved = [t.metrics.get("resolved", False) for t in trajectory_group]
+
+        print(f"Rewards: {rewards}")
+        print(f"Resolved: {sum(resolved)}/{len(resolved)}")
+        print(f"Average reward: {sum(rewards) / len(rewards):.3f}")
+
+        all_trajectories.extend(trajectory_group)
+
+    # Summary statistics
+    print(f"\n{'=' * 60}")
+    print("SUMMARY")
+    print(f"{'=' * 60}")
+    print(f"Total trajectories: {len(all_trajectories)}")
+    print(
+        f"Average reward: {sum(t.reward for t in all_trajectories) / len(all_trajectories):.3f}"
+    )
+    print(
+        f"Resolved: {sum(t.metrics.get('resolved', False) for t in all_trajectories)}/{len(all_trajectories)}"
+    )
+
+    return all_trajectories
+
+
+async def train_trainable_model(
+    base_model: str = "willcb/Qwen3-32B",
+    model_name: str = "001",
+    batch_size: int = 4,
+    rollouts_per_instance: int = 4,
+    num_epochs: int = 1,
+    learning_rate: float = 5e-5,
+    reward_power: float = 1.33,
+    max_concurrent_batches: int = 2,
+    use_quality_filter: bool = True,
+    require_non_zero_tests: bool = True,
+):
+    """Train a model with gradient updates using ART."""
+    from art.local import LocalBackend
+
+    # Initialize backend
+    backend = LocalBackend()
+
+    # Initialize trainable model
+    model = art.TrainableModel(
+        name=model_name,
+        project="swebench-art-style",
+        base_model=base_model,
+        config=ARTModelConfig(),
+        _internal_config=art.dev.InternalModelConfig(
+            engine_args=art.dev.EngineArgs(
+                tensor_parallel_size=torch.cuda.device_count(),
+                gpu_memory_utilization=0.85,
+            ),
+            torchtune_args=art.dev.TorchtuneArgs(
+                model="qwen3_32b",
+                model_type="QWEN3",
+                async_weight_syncing=True,
+            ),
+        ),
+    )
+
+    await model.register(backend)
+
+    # Load instances
+    all_instances_df = get_filtered_swe_smith_instances_df()
+
+    # Apply quality filter if requested
+    if use_quality_filter:
+        instances_df = filter_quality_instances(
+            all_instances_df, require_non_zero_tests=require_non_zero_tests
+        )
+        print(
+            f"Filtered to {len(instances_df)} quality instances from {len(all_instances_df)} total"
+        )
+    else:
+        instances_df = all_instances_df
+        print(f"Using all {len(instances_df)} instances (no quality filter)")
+
+    instances = list(instances_df.sample(fraction=1.0, seed=42).pipe(as_instances_iter))
+
+    print(f"Loaded {len(instances)} instances")
+    print(f"Starting training for {num_epochs} epochs")
+
+    # Training loop
+    for epoch in range(num_epochs):
+        print(f"\n{'=' * 60}")
+        print(f"EPOCH {epoch + 1}/{num_epochs}")
+        print(f"{'=' * 60}")
+
+        # Process instances in batches
+        for batch_start in range(0, len(instances), batch_size):
+            batch_end = min(batch_start + batch_size, len(instances))
+            batch_instances = instances[batch_start:batch_end]
+
+            print(
+                f"\nBatch {batch_start // batch_size + 1}: instances {batch_start}-{batch_end - 1}"
+            )
+
+            # Gather trajectory groups for this batch
+            trajectory_groups = await art.gather_trajectory_groups(
+                (
+                    art.TrajectoryGroup(
+                        rollout_with_sandbox(model, instance, reward_power)
+                        for _ in range(rollouts_per_instance)
+                    )
+                    for instance in batch_instances
+                ),
+                pbar_desc=f"Batch {batch_start // batch_size + 1}",
+            )
+
+            # Log batch statistics
+            all_trajectories = [t for group in trajectory_groups for t in group]
+            avg_reward = sum(t.reward for t in all_trajectories) / len(all_trajectories)
+            resolved_count = sum(
+                t.metrics.get("resolved", False) for t in all_trajectories
+            )
+
+            print(f"Batch average reward: {avg_reward:.3f}")
+            print(f"Batch resolved: {resolved_count}/{len(all_trajectories)}")
+
+            # Train on this batch
+            await model.train(
+                trajectory_groups,
+                config=art.TrainConfig(learning_rate=learning_rate),
+                _config=art.dev.TrainConfig(allow_training_without_logprobs=True),
+                verbose=True,
+            )
+
+            # Log the trajectories
+            await model.log(trajectory_groups)
+
+    print("\nTraining complete!")
+    return model
+
+
+async def main():
+    parser = argparse.ArgumentParser(
+        description="Train SWE-bench models using ART-style rollouts"
+    )
+    parser.add_argument(
+        "--mode",
+        choices=["inference", "train"],
+        default="inference",
+        help="Mode: inference (no gradients) or train (with gradients)",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="Qwen/Qwen3-32B",
+        help="Model name or path",
+    )
+    parser.add_argument(
+        "--api-base",
+        type=str,
+        default="http://localhost:8000/v1",
+        help="API base URL for inference mode",
+    )
+    parser.add_argument(
+        "--num-instances",
+        type=int,
+        default=10,
+        help="Number of instances to use (inference mode)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=4,
+        help="Batch size for training",
+    )
+    parser.add_argument(
+        "--rollouts-per-instance",
+        type=int,
+        default=4,
+        help="Number of rollouts per instance",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=1,
+        help="Number of training epochs (train mode)",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=5e-6,
+        help="Learning rate for training",
+    )
+    parser.add_argument(
+        "--reward-power",
+        type=float,
+        default=1.33,
+        help="Power to apply to progress metric in reward",
+    )
+    parser.add_argument(
+        "--no-quality-filter",
+        action="store_true",
+        help="Disable quality filtering and use all instances (not recommended)",
+    )
+    parser.add_argument(
+        "--require-non-zero-tests",
+        action="store_true",
+        default=True,
+        help="When using quality filter, require instances to have non-zero tests",
+    )
+
+    args = parser.parse_args()
+
+    # Set up environment
+    setup_environment()
+
+    # Run appropriate mode
+    if args.mode == "inference":
+        await train_inference_model(
+            model_name=args.model,
+            api_base=args.api_base,
+            num_instances=args.num_instances,
+            rollouts_per_instance=args.rollouts_per_instance,
+            reward_power=args.reward_power,
+            use_quality_filter=not args.no_quality_filter,
+            require_non_zero_tests=args.require_non_zero_tests,
+        )
+    else:
+        await train_trainable_model(
+            base_model=args.model,
+            batch_size=args.batch_size,
+            rollouts_per_instance=args.rollouts_per_instance,
+            num_epochs=args.epochs,
+            learning_rate=args.learning_rate,
+            reward_power=args.reward_power,
+            use_quality_filter=not args.no_quality_filter,
+            require_non_zero_tests=args.require_non_zero_tests,
+        )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/dev/swebench/uv.lock b/dev/swebench/uv.lock
index 36d17376..59219693 100644
--- a/dev/swebench/uv.lock
+++ b/dev/swebench/uv.lock
@@ -213,6 +213,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
+[[package]]
+name = "airportsdata"
+version = "20250706"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/e0/311f397752cb15d3772c3856924773eaaab905f922f24ef19739a1de67eb/airportsdata-20250706.tar.gz", hash = "sha256:66d7a03e825d592d85ed650f2c1d4b4302d1c04f8f37a15f1eda29a5e03d4af0", size = 903223, upload-time = "2025-07-06T22:19:23.1Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3a/11/f359cbbde87b88b0f17503e9c17cae6dfc9117b756ce600087306f28f52d/airportsdata-20250706-py3-none-any.whl", hash = "sha256:750e882a75e391572ae859d4cb78cb801f5f2ca71b07849a381670cb01780677", size = 912715, upload-time = "2025-07-06T22:19:21.158Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -222,6 +231,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.9.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034, upload-time = "2021-11-06T17:52:23.524Z" }
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -246,6 +261,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "astor"
+version = "0.8.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/21/75b771132fee241dfe601d39ade629548a9626d1d39f333fde31bc46febe/astor-0.8.1.tar.gz", hash = "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e", size = 35090, upload-time = "2019-12-10T01:50:35.51Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl", hash = "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5", size = 27488, upload-time = "2019-12-10T01:50:33.628Z" },
+]
+
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -364,6 +388,88 @@ wheels = [
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+[[package]]
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+    { name = "numpy" },
+    { name = "torch" },
+]
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+    { url = "https://files.pythonhosted.org/packages/85/7d/06da01fac23a5032632dd7874b31c1d9b7b9af2314b2b07e5f99641950da/bitsandbytes-0.46.1-py3-none-win_amd64.whl", hash = "sha256:9f6f61376bd0e9780c5dc4ddee7d1f52cb10fe8034a1ea588611f4e8b87eb6a7", size = 72197544, upload-time = "2025-07-02T19:44:29.483Z" },
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 ]
-provides-extras = ["backend"]
+provides-extras = ["plotting", "backend"]
 
 [package.metadata.requires-dev]
 dev = [
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@@ -2441,9 +3284,10 @@ dependencies = [
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     { name = "langfuse" },
     { name = "modal" },
-    { name = "openpipe-art" },
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 [package.metadata.requires-dev]
@@ -2477,6 +3322,186 @@ dev = [
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+
 [[package]]
 name = "xxhash"
 version = "3.5.0"
diff --git a/dev/swebench/vllm.ipynb b/dev/swebench/vllm.ipynb
index 5ad68a39..9750a6d7 100644
--- a/dev/swebench/vllm.ipynb
+++ b/dev/swebench/vllm.ipynb
@@ -12,9 +12,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".cell-output-ipywidget-background {\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       ":root {\n",
+       "    --jp-widgets-color: var(--vscode-editor-foreground);\n",
+       "    --jp-widgets-font-size: var(--vscode-editor-font-size);\n",
+       "}  \n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%%html\n",
     "<style>\n",
@@ -30,9 +51,333 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO 07-10 18:27:21 [__init__.py:244] Automatically detected platform cuda.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading '.gitattributes' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/52373fe24473b1aa44333d318f578ae6bf04b49b.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/52373fe24473b1aa44333d318f578ae6bf04b49b\n",
+      "Downloading 'README.md' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/bc5f30d6632ac0efdc7be2e9095e9e9579af2e33.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/bc5f30d6632ac0efdc7be2e9095e9e9579af2e33\n",
+      "Downloading 'added_tokens.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/b54f9135e44c1e81047e8d05cb027af8bc039eed.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/b54f9135e44c1e81047e8d05cb027af8bc039eed\n",
+      "Downloading 'chat_template.jinja' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/bdf7919a96cfe43d50914a007b9c0877bd0ec27e.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/bdf7919a96cfe43d50914a007b9c0877bd0ec27e\n",
+      "Downloading 'config.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/206ceb93a00489bf597e91de392b40eef332f6fc.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/206ceb93a00489bf597e91de392b40eef332f6fc\n",
+      "Downloading 'generation_config.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/aae4bea99feafeeb5526928b89ce09695bc6eb08.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/aae4bea99feafeeb5526928b89ce09695bc6eb08\n",
+      "Downloading 'merges.txt' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/31349551d90c7606f325fe0f11bbb8bd5fa0d7c7.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/31349551d90c7606f325fe0f11bbb8bd5fa0d7c7\n",
+      "Downloading 'model-00001-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/e5da875a02c8eb8d139eea2e3a3822a1d8228036724ea76ff2fc1dfc2c8f329a.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/e5da875a02c8eb8d139eea2e3a3822a1d8228036724ea76ff2fc1dfc2c8f329a\n",
+      "Downloading 'model-00002-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/007c079c15cae8ba9acd0104b61b68d9b49ff2631f4ea672e0821e87ac0aa3a7.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/007c079c15cae8ba9acd0104b61b68d9b49ff2631f4ea672e0821e87ac0aa3a7\n",
+      "Downloading 'model-00003-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/56e2b3a80f934c890e5afd7c7a1512b48910f2f45acdc125c370716394c90313.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/56e2b3a80f934c890e5afd7c7a1512b48910f2f45acdc125c370716394c90313\n",
+      "Downloading 'model-00004-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/c8eceb24e1e8cefae1f13e91834ea13ee4e6433624834f9565a1c0f99506e5c9.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/c8eceb24e1e8cefae1f13e91834ea13ee4e6433624834f9565a1c0f99506e5c9\n",
+      "Downloading 'model-00005-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ff78ade9e51143cb64f6ec704faa2b1e5d409f8db01cd6f35976999029f30898.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ff78ade9e51143cb64f6ec704faa2b1e5d409f8db01cd6f35976999029f30898\n",
+      "Downloading 'model-00006-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/f951031c7c5b579c6bb82ee01887f0abede152768783a0d7d8eaa293622c6672.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/f951031c7c5b579c6bb82ee01887f0abede152768783a0d7d8eaa293622c6672\n",
+      "Downloading 'model-00007-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/62dba33c497528a50fcd5c61d137a8f939311084fb08cb42890cf057095692db.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/62dba33c497528a50fcd5c61d137a8f939311084fb08cb42890cf057095692db\n",
+      "Downloading 'model-00008-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/b49614615e7c280726aee39e8e4020574c6250337466b0a182e0c941274d9a9a.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/b49614615e7c280726aee39e8e4020574c6250337466b0a182e0c941274d9a9a\n",
+      "Downloading 'model-00009-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/3c76bfbd34b631879d8c926d923fc718a533e2dc4c9b1dc0e4903663993d8728.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/3c76bfbd34b631879d8c926d923fc718a533e2dc4c9b1dc0e4903663993d8728\n",
+      "Downloading 'model-00010-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/c2cfea0d51e4d1dc78b55e65b21636957bf9c201137c48697a520dd3d28e5551.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/c2cfea0d51e4d1dc78b55e65b21636957bf9c201137c48697a520dd3d28e5551\n",
+      "Downloading 'model-00011-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/397f63feb7cf3bba972fc309a7640a98223e66d51c55558c1dba3c4ed45ed892.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/397f63feb7cf3bba972fc309a7640a98223e66d51c55558c1dba3c4ed45ed892\n",
+      "Downloading 'model-00012-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/1dad948134cc8b8b834899699d44546c7d142ff4d5d8354fa427c18a437cd1b6.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/1dad948134cc8b8b834899699d44546c7d142ff4d5d8354fa427c18a437cd1b6\n",
+      "Downloading 'model-00013-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/a504a2fa85eb4618ef42c5be610f5429c1fc13ad06ac164bc39ff620d19ee8c2.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/a504a2fa85eb4618ef42c5be610f5429c1fc13ad06ac164bc39ff620d19ee8c2\n",
+      "Downloading 'model-00014-of-00014.safetensors' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ae5c47ee8b511f3d2af0428de193f86a905ebcd47d1b4f348b94ccbc706e721d.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ae5c47ee8b511f3d2af0428de193f86a905ebcd47d1b4f348b94ccbc706e721d\n",
+      "Downloading 'model.safetensors.index.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/9c12a5bc60a45cefc1f7bd9a03456ab6d20c2174.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/9c12a5bc60a45cefc1f7bd9a03456ab6d20c2174\n",
+      "Downloading 'special_tokens_map.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ac23c0aaa2434523c494330aeb79c58395378103.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ac23c0aaa2434523c494330aeb79c58395378103\n",
+      "Downloading 'tokenizer.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4\n",
+      "Downloading 'tokenizer_config.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ddaf69808214a44fdd26d3785b66c1367c78277a.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/ddaf69808214a44fdd26d3785b66c1367c78277a\n",
+      "Downloading 'vocab.json' to '/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/4783fe10ac3adce15ac8f358ef5462739852c569.incomplete'\n",
+      "Download complete. Moving file to /home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/blobs/4783fe10ac3adce15ac8f358ef5462739852c569\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/sky/.cache/huggingface/hub/models--willcb--Qwen3-32B/snapshots/4371efdac7e15a639136f218777f6bf3730d1ad2\n",
+      "INFO 07-10 18:28:35 [config.py:823] This model supports multiple tasks: {'reward', 'score', 'embed', 'generate', 'classify'}. Defaulting to 'generate'.\n",
+      "INFO 07-10 18:28:35 [config.py:1946] Defaulting to use mp for distributed inference\n",
+      "INFO 07-10 18:28:35 [config.py:2195] Chunked prefill is enabled with max_num_batched_tokens=2048.\n",
+      "INFO 07-10 18:28:36 [core.py:455] Waiting for init message from front-end.\n",
+      "INFO 07-10 18:28:36 [core.py:70] Initializing a V1 LLM engine (v0.9.1) with config: model='willcb/Qwen3-32B', speculative_config=None, tokenizer='willcb/Qwen3-32B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config={}, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=40960, download_dir=None, load_format=auto, tensor_parallel_size=8, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto,  device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=willcb/Qwen3-32B, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={\"level\":3,\"debug_dump_path\":\"\",\"cache_dir\":\"\",\"backend\":\"\",\"custom_ops\":[\"none\"],\"splitting_ops\":[\"vllm.unified_attention\",\"vllm.unified_attention_with_output\"],\"use_inductor\":true,\"compile_sizes\":[],\"inductor_compile_config\":{\"enable_auto_functionalized_v2\":false},\"inductor_passes\":{},\"use_cudagraph\":true,\"cudagraph_num_of_warmups\":1,\"cudagraph_capture_sizes\":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],\"cudagraph_copy_inputs\":false,\"full_cuda_graph\":false,\"max_capture_size\":512,\"local_cache_dir\":null}\n",
+      "WARNING 07-10 18:28:36 [multiproc_worker_utils.py:307] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.\n",
+      "INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3, 4, 5, 6, 7], buffer_handle=(8, 16777216, 10, 'psm_e0997232'), local_subscribe_addr='ipc:///tmp/d1aff72b-c00e-4332-8ec2-1746cfb4b0d2', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab9a50>\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_252a4bea'), local_subscribe_addr='ipc:///tmp/cfbcfa60-ec15-447a-a83d-13accfc4db6f', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab8b20>\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_d7be156f'), local_subscribe_addr='ipc:///tmp/2bf11dc5-68fc-4eb3-ae36-4f640d5fcb32', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab8940>\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1f8aeffd'), local_subscribe_addr='ipc:///tmp/cdb57267-ea25-400f-bf6e-5289f8d4263b', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab8250>\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_b37d5ffc'), local_subscribe_addr='ipc:///tmp/b7875885-6839-4121-b536-fb8c12e84cb5', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab92a0>\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_856a19e6'), local_subscribe_addr='ipc:///tmp/39492bc0-4827-4ead-83f5-12f846753d31', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "INFO 07-10 18:28:36 [worker_base.py:590] Injected <class 'art.vllm.engine.WorkerExtension'> into <class 'vllm.v1.worker.gpu_worker.Worker'> for extended collective_rpc calls ['run', 'time']\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab9420>\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_07529882'), local_subscribe_addr='ipc:///tmp/a17579da-964f-4183-800b-b9f93bbc08a3', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab9630>\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_10729a81'), local_subscribe_addr='ipc:///tmp/94c29a67-d8df-4895-a6d6-10e61d7dc57a', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "WARNING 07-10 18:28:36 [utils.py:2737] Methods determine_num_available_blocks,device_config,get_cache_block_size_bytes,initialize_cache not implemented in <vllm.v1.worker.gpu_worker.Worker object at 0x79e272ab9c60>\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:36 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_11a6fe30'), local_subscribe_addr='ipc:///tmp/7829518e-99b9-4b82-80e9-d39974f6eb6d', remote_subscribe_addr=None, remote_addr_ipv6=False)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[W710 18:28:46.726363006 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.816596278 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.816955430 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.976976145 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.977255207 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.124608631 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.125017404 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.641564419 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.641975251 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.684606516 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.686093893 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.733199500 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:47.733529884 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:48.776077801 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:48.776291606 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:48.777544266 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[W710 18:28:48.820161000 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:48 [utils.py:1126] Found nccl from library libnccl.so.2\n",
+      "INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m \u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "INFO 07-10 18:28:48 [pynccl.py:70] vLLM is using nccl==2.26.2\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m \u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "INFO 07-10 18:28:53 [custom_all_reduce_utils.py:246] reading GPU P2P access cache from /home/sky/.cache/vllm/gpu_p2p_access_cache_for_0,1,2,3,4,5,6,7.json\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:53 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3, 4, 5, 6, 7], buffer_handle=(7, 4194304, 6, 'psm_e2a537e4'), local_subscribe_addr='ipc:///tmp/825508b3-1e36-4abd-ace8-0edd7eb7a392', remote_subscribe_addr=None, remote_addr_ipv6=False)\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m \u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:53 [parallel_state.py:1065] rank 0 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 0, EP rank 0\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:53 [parallel_state.py:1065] rank 1 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 1, EP rank 1\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 2 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 2, EP rank 2\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 7 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 7, EP rank 7\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 3 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 3, EP rank 3\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 6 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 6, EP rank 6\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 5 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 5, EP rank 5\n",
+      "INFO 07-10 18:28:53 [parallel_state.py:1065] rank 4 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 4, EP rank 4\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "WARNING 07-10 18:28:53 [topk_topp_sampler.py:59] FlashInfer is not available. Falling back to the PyTorch-native implementation of top-p & top-k sampling. For the best performance, please install FlashInfer.\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m \u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m \u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1595] Starting to load model willcb/Qwen3-32B...\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:53 [gpu_model_runner.py:1600] Loading model from scratch...\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:53 [cuda.py:252] Using Flash Attention backend on V1 engine.\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:53 [weight_utils.py:292] Using model weights format ['*.safetensors']\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c2b691a85e74f02a18d4cddecd6cb17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading safetensors checkpoint shards:   0% Completed | 0/14 [00:00<?, ?it/s]\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:56 [default_loader.py:272] Loading weights took 2.84 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:56 [default_loader.py:272] Loading weights took 2.84 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:56 [default_loader.py:272] Loading weights took 2.99 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:57 [default_loader.py:272] Loading weights took 2.86 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:57 [default_loader.py:272] Loading weights took 2.80 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:57 [default_loader.py:272] Loading weights took 2.93 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:57 [default_loader.py:272] Loading weights took 3.40 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.295183 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.346557 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.393145 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.422566 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.470878 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:57 [default_loader.py:272] Loading weights took 3.31 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.752320 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.664498 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:28:57 [gpu_model_runner.py:1624] Model loading took 7.6871 GiB and 3.929632 seconds\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_6_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.09 s\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_1_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.10 s\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_3_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m \u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_5_0 for vLLM's torch.compile\n",
+      "INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.18 s\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.17 s\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_0_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.20 s\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_2_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.24 s\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_4_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.22 s\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:462] Using cache directory: /home/sky/.cache/vllm/torch_compile_cache/af2be87ed6/rank_7_0 for vLLM's torch.compile\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:29:09 [backends.py:472] Dynamo bytecode transform time: 11.35 s\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:29:13 [backends.py:161] Cache the graph of shape None for later use\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:29:55 [backends.py:173] Compiling a graph for general shape takes 45.51 s\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:29:55 [backends.py:173] Compiling a graph for general shape takes 46.03 s\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:29:55 [backends.py:173] Compiling a graph for general shape takes 45.86 s\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:29:56 [backends.py:173] Compiling a graph for general shape takes 46.80 s\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:29:57 [backends.py:173] Compiling a graph for general shape takes 47.14 s\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:29:57 [backends.py:173] Compiling a graph for general shape takes 47.30 s\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:29:57 [backends.py:173] Compiling a graph for general shape takes 47.29 s\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:29:57 [backends.py:173] Compiling a graph for general shape takes 47.50 s\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 56.60 s in total\n",
+      "INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 57.91 s in total\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m \u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 58.53 s in total\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 57.21 s in total\n",
+      "INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 57.20 s in total\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 58.71 s in total\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 58.36 s in total\n",
+      "INFO 07-10 18:30:24 [monitor.py:34] torch.compile takes 58.48 s in total\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 110.33 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.96 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:30:27 [gpu_worker.py:227] Available KV cache memory: 109.86 GiB\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,603,120 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.97x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,600,048 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 87.89x\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:715] GPU KV cache size: 3,615,408 tokens\n",
+      "INFO 07-10 18:30:28 [kv_cache_utils.py:719] Maximum concurrency for 40,960 tokens per request: 88.27x\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:31:03 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:31:03 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:31:03 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m INFO 07-10 18:31:09 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m INFO 07-10 18:31:09 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:31:09 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:31:09 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:31:11 [custom_all_reduce.py:196] Registering 8643 cuda graph addresses\n",
+      "\u001b[1;36m(VllmWorker rank=7 pid=55327)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=5 pid=55305)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=6 pid=55317)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=4 pid=55292)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=3 pid=55278)\u001b[0;0m \u001b[1;36m(VllmWorker rank=1 pid=55261)\u001b[0;0m \u001b[1;36m(VllmWorker rank=0 pid=55258)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "\u001b[1;36m(VllmWorker rank=2 pid=55272)\u001b[0;0m INFO 07-10 18:31:11 [gpu_model_runner.py:2048] Graph capturing finished in 44 secs, took 1.13 GiB\n",
+      "INFO 07-10 18:31:11 [core.py:171] init engine (profile, create kv cache, warmup model) took 134.02 seconds\n",
+      "INFO 07-10 18:31:12 [loggers.py:137] Engine 000: vllm cache_config_info with initialization after num_gpu_blocks is: 225003\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "import vllm\n",
@@ -41,11 +386,11 @@
     "\n",
     "llm = await get_llm(\n",
     "    vllm.AsyncEngineArgs(\n",
-    "        model=\"Qwen/Qwen3-32B\",\n",
+    "        model=\"willcb/Qwen3-32B\",\n",
     "        # pipeline_parallel_size=1,\n",
     "        tensor_parallel_size=torch.cuda.device_count(),\n",
     "        # data_parallel_size=1,\n",
-    "        enforce_eager=True,\n",
+    "        # enforce_eager=True,\n",
     "        generation_config=\"vllm\",\n",
     "    ),\n",
     ")"
@@ -53,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,6 +424,27 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sleep_task' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msleep_task\u001b[49m\u001b[38;5;241m.\u001b[39mcancel()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sleep_task' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "sleep_task.cancel()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -145,9 +511,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[rank5]:[W710 18:26:52.260806609 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[rank5]:[W710 18:26:52.256495309 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=255, addr=[fdff:ffff::3daf:5f6c:207d:0]:22879, remote=[fdff:ffff::]:3661): failed to recv, got 0 bytes\n",
+      "Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):\n",
+      "frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x7d20584d35e8 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libc10.so)\n",
+      "frame #1: <unknown function> + 0x5ba8afe (0x7d202d5abafe in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #2: <unknown function> + 0x5baae40 (0x7d202d5ade40 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #3: <unknown function> + 0x5bab74a (0x7d202d5ae74a in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x7d202d5a81a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x7d1fea7a19a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)\n",
+      "frame #6: <unknown function> + 0xdbbf4 (0x7d206c622bf4 in /home/sky/miniconda3/bin/../lib/libstdc++.so.6)\n",
+      "frame #7: <unknown function> + 0x8609 (0x7d206d1db609 in /lib/x86_64-linux-gnu/libpthread.so.0)\n",
+      "frame #8: clone + 0x43 (0x7d206cfa6353 in /lib/x86_64-linux-gnu/libc.so.6)\n",
+      "\n",
+      "[rank5]:[W710 18:26:52.260891862 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0 Rank 5] Failed to check the \"should dump\" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes\n",
+      "[rank6]:[W710 18:26:52.261004155 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[rank6]:[W710 18:26:52.256495247 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=259, addr=[fdff:ffff::3daf:5f6c:207d:0]:22879, remote=[fdff:ffff::]:3669): failed to recv, got 0 bytes\n",
+      "Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):\n",
+      "frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x7d20584d35e8 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libc10.so)\n",
+      "frame #1: <unknown function> + 0x5ba8afe (0x7d202d5abafe in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #2: <unknown function> + 0x5baae40 (0x7d202d5ade40 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #3: <unknown function> + 0x5bab74a (0x7d202d5ae74a in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x7d202d5a81a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x7d1fea7a19a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)\n",
+      "frame #6: <unknown function> + 0xdbbf4 (0x7d206c622bf4 in /home/sky/miniconda3/bin/../lib/libstdc++.so.6)\n",
+      "frame #7: <unknown function> + 0x8609 (0x7d206d1db609 in /lib/x86_64-linux-gnu/libpthread.so.0)\n",
+      "frame #8: clone + 0x43 (0x7d206cfa6353 in /lib/x86_64-linux-gnu/libc.so.6)\n",
+      "\n",
+      "[rank6]:[W710 18:26:52.261071993 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0 Rank 6] Failed to check the \"should dump\" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes\n",
+      "[rank7]:[W710 18:26:52.261724878 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[rank7]:[W710 18:26:52.256910756 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=263, addr=[fdff:ffff::3daf:5f6c:207d:0]:22879, remote=[fdff:ffff::]:3658): failed to recv, got 0 bytes\n",
+      "Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):\n",
+      "frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x7d20584d35e8 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libc10.so)\n",
+      "frame #1: <unknown function> + 0x5ba8afe (0x7d202d5abafe in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #2: <unknown function> + 0x5baae40 (0x7d202d5ade40 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #3: <unknown function> + 0x5bab74a (0x7d202d5ae74a in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x7d202d5a81a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x7d1fea7a19a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)\n",
+      "frame #6: <unknown function> + 0xdbbf4 (0x7d206c622bf4 in /home/sky/miniconda3/bin/../lib/libstdc++.so.6)\n",
+      "frame #7: <unknown function> + 0x8609 (0x7d206d1db609 in /lib/x86_64-linux-gnu/libpthread.so.0)\n",
+      "frame #8: clone + 0x43 (0x7d206cfa6353 in /lib/x86_64-linux-gnu/libc.so.6)\n",
+      "\n",
+      "[rank7]:[W710 18:26:52.261763071 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0 Rank 7] Failed to check the \"should dump\" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes\n",
+      "[rank4]:[W710 18:26:52.386814483 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[rank4]:[W710 18:26:52.383335839 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=251, addr=[fdff:ffff::3daf:5f6c:207d:0]:22879, remote=[fdff:ffff::]:3661): failed to recv, got 0 bytes\n",
+      "Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):\n",
+      "frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x7d20584d35e8 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libc10.so)\n",
+      "frame #1: <unknown function> + 0x5ba8afe (0x7d202d5abafe in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #2: <unknown function> + 0x5baae40 (0x7d202d5ade40 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #3: <unknown function> + 0x5bab74a (0x7d202d5ae74a in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x7d202d5a81a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x7d1fea7a19a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)\n",
+      "frame #6: <unknown function> + 0xdbbf4 (0x7d206c622bf4 in /home/sky/miniconda3/bin/../lib/libstdc++.so.6)\n",
+      "frame #7: <unknown function> + 0x8609 (0x7d206d1db609 in /lib/x86_64-linux-gnu/libpthread.so.0)\n",
+      "frame #8: clone + 0x43 (0x7d206cfa6353 in /lib/x86_64-linux-gnu/libc.so.6)\n",
+      "\n",
+      "[rank1]:[W710 18:26:52.386860565 socket.cpp:200] [c10d] The hostname of the client socket cannot be retrieved. err=-3\n",
+      "[rank1]:[W710 18:26:52.383376864 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=241, addr=[fdff:ffff::3daf:5f6c:207d:0]:22879, remote=[fdff:ffff::]:3661): failed to recv, got 0 bytes\n",
+      "Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):\n",
+      "frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x7d20584d35e8 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libc10.so)\n",
+      "frame #1: <unknown function> + 0x5ba8afe (0x7d202d5abafe in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #2: <unknown function> + 0x5baae40 (0x7d202d5ade40 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #3: <unknown function> + 0x5bab74a (0x7d202d5ae74a in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x7d202d5a81a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so)\n",
+      "frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x7d1fea7a19a9 in /home/sky/sky_workdir/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)\n",
+      "frame #6: <unknown function> + 0xdbbf4 (0x7d206c622bf4 in /home/sky/miniconda3/bin/../lib/libstdc++.so.6)\n",
+      "frame #7: <unknown function> + 0x8609 (0x7d206d1db609 in /lib/x86_64-linux-gnu/libpthread.so.0)\n",
+      "frame #8: clone + 0x43 (0x7d206cfa6353 in /lib/x86_64-linux-gnu/libc.so.6)\n",
+      "\n",
+      "[rank4]:[W710 18:26:52.386877927 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0 Rank 4] Failed to check the \"should dump\" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes\n",
+      "[rank1]:[W710 18:26:52.386912268 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0 Rank 1] Failed to check the \"should dump\" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes\n"
+     ]
+    }
+   ],
    "source": [
     "llm.shutdown()"
    ]