docs(paper-oct-14): add papers (#80)

add paper and update browsecomp config

Author: BinWang28

config/agent_browsecomp-en_mirothinker.yaml

@@ -22,6 +22,11 @@ main_agent:
 
   tool_config:
     - tool-reasoning
+    - tool-searching
+    - tool-image-video
+    - tool-reading
+    - tool-code
+    - tool-audio
 
   max_turns: 50  # Maximum number of turns for main agent execution
   max_tool_calls_per_turn: 10  # Maximum number of tool calls per turn
@@ -40,32 +45,7 @@ main_agent:
   chinese_context: "${oc.env:CHINESE_CONTEXT,false}"
 
 
-sub_agents:
-  agent-worker:
-    prompt_class: SubAgentWorkerPrompt
-    llm: 
-      provider_class: "MiroThinkerSGLangClient"
-      model_name: "DUMMY_MODEL_NAME"
-      async_client: true
-      temperature: 0.3
-      top_p: 1.0
-      min_p: 0.0
-      top_k: -1
-      max_tokens: 4096
-      oai_mirothinker_api_key: "${oc.env:OAI_MIROTHINKER_API_KEY,dummy_key}"
-      oai_mirothinker_base_url: "${oc.env:OAI_MIROTHINKER_BASE_URL,http://localhost:61005/v1}"
-      keep_tool_result: -1
-      oai_tool_thinking: false
-    
-    tool_config:
-      - tool-searching
-      - tool-image-video
-      - tool-reading
-      - tool-code
-      - tool-audio
-
-    max_turns: 50  # Maximum number of turns for main agent execution
-    max_tool_calls_per_turn: 10  # Maximum number of tool calls per turn
+sub_agents: null
 
 
 # Can define some top-level or default parameters here

docs/mkdocs/docs/all_about_agents.md

@@ -239,6 +239,34 @@ Welcome to our comprehensive resource collection for AI agents. This page curate
 **P073** - Auto-scaling Continuous Memory for GUI Agent
     - [:material-file-document: Paper](https://arxiv.org/abs/2510.09038)
 
+**P074** - StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.11618)
+
+**P075** - WebRouter: Query-specific Router via Variational Information Bottleneck for Cost-sensitive Web Agent
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.11221)
+
+**P076** - LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10890)
+
+**P077** - BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10666)
+
+**P078** - AGENTIQL: An Agent-Inspired Multi-Expert Framework for Text-to-SQL Generation
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10661)
+
+**P079** - FML-bench: A Benchmark for Automatic ML Research Agents Highlighting the Importance of Exploration Breadth
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10472)
+
+**P080** - MedAgentAudit: Diagnosing and Quantifying Collaborative Failure Modes in Medical Multi-Agent Systems
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10185)
+
+**P081** - Can Tool-Integrated Reinforcement Learning Generalize Across Diverse Domains?
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.11184)
+
+**P082** - A Survey on Agentic Multimodal Large Language Models
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.10991)
+
+
 
 ---
 
@@ -361,6 +389,12 @@ Welcome to our comprehensive resource collection for AI agents. This page curate
 **E029** - DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation
     - [:material-file-document: Paper](https://arxiv.org/abs/2510.09116)
 
+**E030** - When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.11695)
+
+**E031** - A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.09721)
+
 
 ---
 
@@ -420,6 +454,8 @@ Welcome to our comprehensive resource collection for AI agents. This page curate
 **M017** - Mem-α: Learning Memory Construction via Reinforcement Learning
     - [:material-file-document: Paper](https://arxiv.org/abs/2509.25911)
 
+**M018** - Preference-Aware Memory Update for Long-Term LLM Agents
+    - [:material-file-document: Paper](https://arxiv.org/abs/2510.09720)
 
 ---