RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs

2026-08-29

AI Summary

为解决大语言模型在处理多跳知识推理任务时的幻觉问题，提出RACER框架，通过强化学习和多智能体协作从知识图谱中提取高质量推理路径。

Abstract

Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While integrating Knowledge Graphs (KGs) provides a structured and verifiable information source, current KG-enhanced LLM paradigms usually rely on single-agent path extraction and fixed prompting, lacking adaptability and facing huge search spaces. To address these challenges, we propose RACER, a Reinforced Agent Collaboration framework for Explainable Reasoning on knowledge graphs. RACER employs a semantic-aware action pruning and teacher-guided reinforcement learning mechanism to efficiently extract high-quality reasoning pathways from large-scale KGs. Furthermore, to mitigate single-path generation pitfalls, we introduce a cross-task accumulated shared memory graph paired with an attention-driven multi-path knowledge refinement module. Finally, RACER orchestrates these components through a four-role multi-agent collaboration system (GraphAgent, TemplateAgent, AnswerAgent, and CriticAgent) to dynamically refine prompts and evaluate answers. Extensive experiments on CommonsenseQA and OpenBookQA datasets demonstrate that RACER significantly outperforms state-of-the-art KG-enhanced LLM baselines with an average improvement of 5\%, offering robust and highly interpretable reasoning capabilities.

Problem

• Large Language Models
• Knowledge Graphs
• Complex Reasoning
• Multi-hop Domain Knowledge
• Single-agent Path Extraction

Innovation

• Reinforced Agent Collaboration
• Explainable Reasoning
• Knowledge Graphs
• Semantic-aware Action Pruning
• Cross-task Accumulated Shared Memory