Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving

• URL: http://arxiv.org/abs/2507.06229v4
• Date: Mon, 21 Jul 2025 18:52:58 GMT
• Title: Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving
• Authors: Xiangru Tang, Tianrui Qin, Tianhao Peng, Ziyang Zhou, Daniel Shao, Tingting Du, Xinming Wei, Peng Xia, Fang Wu, He Zhu, Ge Zhang, Jiaheng Liu, Xingyao Wang, Sirui Hong, Chenglin Wu, Hao Cheng, Chi Wang, Wangchunshu Zhou,
• Abstract summary: Agent KB captures both high-level problem-solving strategies and detailed execution lessons.<n>Student agents retrieve workflow-level patterns for strategic guidance while teacher agents identify execution-level patterns for refinement.
• Score: 40.51373344437501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current AI agents cannot effectively learn from each other's problem-solving experiences or use past successes to guide self-reflection and error correction in new tasks. We introduce Agent KB, a shared knowledge base that captures both high-level problem-solving strategies and detailed execution lessons, enabling knowledge transfer across agent frameworks. Agent KB implements a novel teacher-student dual-phase retrieval mechanism where student agents retrieve workflow-level patterns for strategic guidance while teacher agents identify execution-level patterns for refinement. This hierarchical approach enables agents to break out of limited reasoning pathways by incorporating diverse strategies from external sources. Evaluations on the GAIA benchmark demonstrate substantial performance gains, with Agent KB improving success rates by up to 6.06 percentage points overall under pass@1. For SWE-bench code repair tasks, our system significantly improved resolution rates, with o3-mini achieving an 8.67 percentage point gain (23 percent to 31.67 percent) in pass@1. Our ablation studies demonstrate that the refinement module proves most critical, with its removal causing a 3.85% drop on challenging Level 3 tasks, highlighting that effective knowledge transfer necessitates both strategic guidance and execution-level refinement.

Related papers

• RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents [43.806220882212386]
  RLVMR integrates dense, process-level supervision into end-to-end RL by rewarding verifiable, meta-reasoning behaviors.<n>On the challenging ALFWorld and ScienceWorld benchmarks, RLVMR achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-07-30T17:00:48Z)
• OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation [65.15955645757705]
  We introduce Workforce, a hierarchical multi-agent framework that decouples strategic planning from specialized execution.<n>During inference, Workforce seamlessly adapts to new domains by adding or modifying worker agents.<n>For training, we introduce optimized Workforce Learning (OWL), which improves generalization across domains.
  arXiv  Detail & Related papers  (2025-05-29T17:51:58Z)
• Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents [10.378745306569053]
  VulTrial is a courtroom-inspired framework designed to enhance automated vulnerability detection.<n>It employs four role-specific agents, which are security researcher, code author, moderator, and review board.<n>Using GPT-3.5 and GPT-4o, VulTrial improves the performance by 102.39% and 84.17% over its respective baselines.
  arXiv  Detail & Related papers  (2025-05-16T07:54:10Z)
• Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks [11.125564622217892]
  Large Language Model agents improve by learning from their own successful experiences without human intervention.<n>Our method constructs and refines a database of self-generated trajectories that serve as in-context examples for future tasks.<n>Our trajectory bootstrapping technique demonstrates that agents can autonomously improve through experience, offering a scalable alternative to labor-intensive knowledge engineering.
  arXiv  Detail & Related papers  (2025-05-01T00:48:12Z)
• Exploring Expert Failures Improves LLM Agent Tuning [74.0772570556016]
  We propose Exploring Expert Failures (EEF), which identifies beneficial actions from failed expert trajectories.<n>EEF successfully solves some previously unsolvable subtasks and improves agent tuning performance.
  arXiv  Detail & Related papers  (2025-04-17T17:53:54Z)
• A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning [7.923577336744156]
  We propose a dual-agent adversarial policy learning framework.<n>This framework allows agents to spontaneously learn the underlying semantics without introducing any human prior knowledge.<n>Experiments show that the adversarial process significantly improves the generalization performance of both agents.
  arXiv  Detail & Related papers  (2025-01-29T02:36:47Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process.<n>We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents [106.87436596397816]
  Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. We propose DEI (Diversity Empowered Intelligence), a framework that leverages their unique expertise. Experiments show that a DEI-guided committee of agents is able to surpass the best individual agent's performance by a large margin.
  arXiv  Detail & Related papers  (2024-08-13T17:50:28Z)
• On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents [58.79302663733703]
  Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents.<n>The impact of clumsy or even malicious agents--those who frequently make errors in their tasks--on the overall performance of the system remains underexplored.<n>This paper investigates what is the resilience of various system structures under faulty agents on different downstream tasks.
  arXiv  Detail & Related papers  (2024-08-02T03:25:20Z)
• Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement [50.481380478458945]
  Iterative step-level Process Refinement (IPR) framework provides detailed step-by-step guidance to enhance agent training. Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.
  arXiv  Detail & Related papers  (2024-06-17T03:29:13Z)
• Devil's Advocate: Anticipatory Reflection for LLM Agents [53.897557605550325]
  Our approach prompts LLM agents to decompose a given task into manageable subtasks. We implement a three-fold introspective intervention:. Anticipatory reflection on potential failures and alternative remedy before action execution. Post-action alignment with subtask objectives and backtracking with remedy to ensure utmost effort in plan execution.
  arXiv  Detail & Related papers  (2024-05-25T19:20:15Z)
• 360$^\circ$REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System [71.96888731208838]
  We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance.<n>We propose Reusable Experience Accumulation with 360$circ$ Assessment (360$circ$REA), a hierarchical multi-agent framework inspired by corporate organizational practices.
  arXiv  Detail & Related papers  (2024-04-08T14:43:13Z)
• Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering [42.277248862366164]
  Triad is a unified framework that utilizes an LLM-based agent with three roles for KBQA tasks. Our framework is executed in four phases, involving the collaboration of the agent's multiple roles.
  arXiv  Detail & Related papers  (2024-02-22T06:23:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.