Agent 研究周度深度综述（2026-10-12 ~ 2026-10-18）

数据来源：papers.cool/arxiv/cs.AI · 生成时间：2026/10/16 17:00:03
本报告聚合本周 arXiv cs.AI 的 Agent 相关论文，跨天去重后深度分析。

本周总览

本周去重后共 33 篇论文，覆盖 4 天数据。上周 73 篇，环比减少 40 篇。

研究方向分布

方向 本周 上周 变化
其他 12 25 -13
记忆系统 6 4 +2
规划推理 6 18 -12
安全对齐 5 9 -4
工程架构 3 6 -3
多智能体 3 4 -1
评估基准 2 16 -14
工具使用 1 0 +1
自我进化 1 9 -8

应用场景分布

场景 论文数 占比
信息检索与问答 3 9%
决策支持 3 9%
企业自动化 2 6%
机器人与物理世界 2 6%
代码开发 1 3%

核心论文解读

1. Who Improves the Robot? Toward Physical Recursive Self-Improvement

  • 英文标题: Who Improves the Robot? Toward Physical Recursive Self-Improvement
  • arXiv: 2610.18842 Kimi解读
  • 方向: 安全对齐 · 自我进化
  • 场景: 机器人与物理世界

2. Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners

  • 英文标题: Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
  • arXiv: 2610.18547 Kimi解读
  • 方向: 规划推理 · 评估基准
  • 场景: 信息检索与问答

3. RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics

  • 英文标题: RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics
  • arXiv: 2610.13889 Kimi解读
  • 方向: 记忆系统 · 工具使用

4. A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning

  • 英文标题: A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning
  • arXiv: 2610.13744 Kimi解读
  • 方向: 规划推理
  • 场景: 信息检索与问答

5. Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify

  • 英文标题: Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify
  • arXiv: 2610.13632 Kimi解读
  • 方向: 其他
  • 场景: 企业自动化

6. MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures

  • 英文标题: MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures
  • arXiv: 2610.13534 Kimi解读
  • 方向: 其他
  • 场景: 机器人与物理世界

7. A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance

  • 英文标题: A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance
  • arXiv: 2610.13479 Kimi解读
  • 方向: 安全对齐 · 工程架构

8. Self-Evolving Agent Harness

  • 英文标题: Self-Evolving Agent Harness
  • arXiv: 2610.18813 Kimi解读
  • 方向: 其他
  • 场景: 企业自动化

9. Beyond the Verdict: Agentic Code Judging with Verifiable Feedback

  • 英文标题: Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
  • arXiv: 2610.18760 Kimi解读
  • 方向: 其他
  • 场景: 代码开发

10. Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents

  • 英文标题: Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents
  • arXiv: 2610.18684 Kimi解读
  • 方向: 记忆系统
  • 场景: 信息检索与问答

研究趋势

主导方向：其他（12 篇），较上周（25 篇）下降。

上升: 记忆系统（4→6）、工具使用（0→1）

下降: 评估基准（16→2）、工程架构（6→3）、其他（25→12）、规划推理（18→6）、自我进化（9→1）、多智能体（4→3）、安全对齐（9→5）

技术演进脉络

其他（12 篇）

记忆系统（6 篇）

规划推理（6 篇）

安全对齐（5 篇）

工程架构（3 篇）

多智能体（3 篇）

评估基准（2 篇）

工具使用（1 篇）

自我进化（1 篇）

工程实践启示

  • 工程架构方向 3 篇，关注系统设计与可扩展性。
  • 工具使用方向 1 篇，function calling 与工具链持续演进。
  • 记忆系统方向 6 篇，RAG 与长期记忆方案不断优化。
  • 多智能体方向 3 篇，协作模式从简单分工走向复杂协调。
  • 安全方向 5 篇，Agent 安全从外部围栏走向内化机制。

下周关注

持续热点：其他（本周 12 篇，上周 25 篇）、记忆系统（本周 6 篇，上周 4 篇）、规划推理（本周 6 篇，上周 18 篇）、安全对齐（本周 5 篇，上周 9 篇）、工程架构（本周 3 篇，上周 6 篇）、多智能体（本周 3 篇，上周 4 篇）、评估基准（本周 2 篇，上周 16 篇）

新出现方向：工具使用（1 篇）

附录：本周论文完整列表

去重后共 33 篇。

2026-10-12（15 篇）

  1. RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Kimi解读 — memory, tool
  2. X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems Kimi解读 — safety
  3. A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Kimi解读 — planning
  4. A Baseline Assessment for the Agentic Web Technology Stack Kimi解读 — engineering
  5. A Step Harder, Not a Step Too Far: The Benefits and Limits of Curriculum Difficulty Progression in GRPO Kimi解读 — other
  6. A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models Kimi解读 — multi_agent
  7. Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify Kimi解读 — other
  8. MENTOR: Learning from Failure with On-Policy Pedagogical Intervention Kimi解读 — other
  9. Constructing Pareto Frontiers of Optimal Reasoning Termination Methods for Overthinking in Reasoning Models Kimi解读 — planning
  10. MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures Kimi解读 — other
  11. Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis Kimi解读 — memory
  12. Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits Kimi解读 — multi_agent
  13. A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance Kimi解读 — safety, engineering
  14. Agentic Large Language Models Kimi解读 — other
  15. Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits Kimi解读 — other

2026-10-13（15 篇）

  1. RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics Kimi解读 — memory, tool
  2. X-AgentRed: Red-Teaming Personal Agents in Evolving Multi-Service Ecosystems Kimi解读 — safety
  3. A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning Kimi解读 — planning
  4. A Baseline Assessment for the Agentic Web Technology Stack Kimi解读 — engineering
  5. A Step Harder, Not a Step Too Far: The Benefits and Limits of Curriculum Difficulty Progression in GRPO Kimi解读 — other
  6. A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models Kimi解读 — multi_agent
  7. Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify Kimi解读 — other
  8. MENTOR: Learning from Failure with On-Policy Pedagogical Intervention Kimi解读 — other
  9. Constructing Pareto Frontiers of Optimal Reasoning Termination Methods for Overthinking in Reasoning Models Kimi解读 — planning
  10. MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures Kimi解读 — other
  11. Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis Kimi解读 — memory
  12. Humanize MLE Agents: Multi-Agent Session Alternation Beyond Single-Agent Limits Kimi解读 — multi_agent
  13. A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance Kimi解读 — safety, engineering
  14. Agentic Large Language Models Kimi解读 — other
  15. Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits Kimi解读 — other

2026-10-14（18 篇）

  1. Who Improves the Robot? Toward Physical Recursive Self-Improvement Kimi解读 — safety, evolution
  2. Self-Evolving Agent Harness Kimi解读 — other
  3. Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents Kimi解读 — other
  4. BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems Kimi解读 — multi_agent
  5. Beyond the Verdict: Agentic Code Judging with Verifiable Feedback Kimi解读 — other
  6. Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents Kimi解读 — memory
  7. To Align or Not To Align: Check Your COMPASS Before You Train Kimi解读 — safety
  8. Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents Kimi解读 — other
  9. Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces Kimi解读 — planning, safety
  10. AgentStripes: An Evidence-Graded Maturity Model for AI Agents Kimi解读 — other
  11. Auditing Future-Memory Use in Point-in-Time Language Agents Kimi解读 — memory
  12. Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling Kimi解读 — planning, evaluation
  13. Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops Kimi解读 — other
  14. LLM Agents for Automated Post-Training via Structured Recipe Optimization Kimi解读 — engineering
  15. Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models Kimi解读 — planning
  16. Configuring RAG Systems: What Breaks, Where Originates, and How to Fix Kimi解读 — memory
  17. Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners Kimi解读 — planning, evaluation
  18. Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale Kimi解读 — memory

2026-10-15（18 篇）

  1. Who Improves the Robot? Toward Physical Recursive Self-Improvement Kimi解读 — safety, evolution
  2. Self-Evolving Agent Harness Kimi解读 — other
  3. Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents Kimi解读 — other
  4. BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems Kimi解读 — multi_agent
  5. Beyond the Verdict: Agentic Code Judging with Verifiable Feedback Kimi解读 — other
  6. Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents Kimi解读 — memory
  7. To Align or Not To Align: Check Your COMPASS Before You Train Kimi解读 — safety
  8. Can an Agent Learn to Predict Its Own Failure? Outcome-Grounded Confidence Calibration in LLM Agents Kimi解读 — other
  9. Beyond Answer Voting: Fine-Grained Correction of LLM Reasoning through Graph Alignment across Sampled Traces Kimi解读 — planning, safety
  10. AgentStripes: An Evidence-Graded Maturity Model for AI Agents Kimi解读 — other
  11. Auditing Future-Memory Use in Point-in-Time Language Agents Kimi解读 — memory
  12. Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling Kimi解读 — planning, evaluation
  13. Reading Right, Acting Wrong: Diagnosing Decision Failures of System One Models in Agent Loops Kimi解读 — other
  14. LLM Agents for Automated Post-Training via Structured Recipe Optimization Kimi解读 — engineering
  15. Order-Consistent Latent Geometry for Reliable Short-Horizon Planning in World Models Kimi解读 — planning
  16. Configuring RAG Systems: What Breaks, Where Originates, and How to Fix Kimi解读 — memory
  17. Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners Kimi解读 — planning, evaluation
  18. Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale Kimi解读 — memory

本报告由 OpenClaw 自动生成，基于 agent-papers-research 每日数据聚合。论文标题和摘要由 GLM-5 翻译生成。

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