用大模型辅助搜索知识图谱，找药物与疾病关联的多步解释。
LLM-Guided Monte Carlo Tree Search over Knowledge Graphs: Composing Mechanistic Explanations for Drug-Disease Pairs
- 大模型只做局部判断，不自主生成路径，结合图结构约束搜索。
- 在两个知识图谱上验证，能发现符合生物学的替代机制。
- 适合需要可解释推理的医药研究者，尤其关注药物作用机理。
从知识图谱中提取多步解释面临组合爆炸问题，需兼顾启发式引导（随深度增长）和信用分配（长序列质量）。前沿大模型在知识与推理任务表现优异，但其知识不可靠，且链式推理性能随长度下降。为此，我们提出TESSERA——一个三部分神经符号框架，将大模型限定于局部判别性判断，而非自主生成；知识图谱定义假设空间，施加严格的结构约束；蒙特卡洛树搜索则通过反向传播实现长程搜索与信用分配。大模型同时充当先验策略以引导探索，并作为状态评估器提供奖励信号。在两个互补的知识图谱上对药物机制解析的评估表明，该方法在保持与人工标注生物学一致性的前提下，发现了连贯的替代机制，消融实验确认了大模型双角色的判别性贡献。本框架可推广至结构化知识上的组合推理任务。
原文摘要 · Abstract (English)
Extracting multi-step explanations from knowledge graphs poses a combinatorial challenge requiring both heuristic guidance (as candidates proliferate with depth) and credit assignment (as path quality emerges over extended sequences). Frontier LLMs, strong on knowledge/reasoning benchmarks, offer a compelling source of such heuristics, yet their knowledge comes sans guarantees and compositional performance degrades as chains lengthen. We thus present TESSERA, a 3-part neuro-symbolic framework that uses LLMs in a circumscribed role: for local discriminative judgement rather than autonomous multi-step generation; the knowledge graph then defines the hypothesis space enforcing hard structural constraints, and MCTS coordinates the long-horizon search with principled credit assignment via backpropagation. LLMs perform dual roles as a prior policy biasing exploration and a comparative state evaluator supplying reward signals. Evaluation on drug mechanism elucidation across two complementary knowledge graphs demonstrates fidelity to curated biology while surfacing coherent alternative mechanisms, with ablations confirming discriminative contribution from both LLM components. Beyond its current application, our framework offers a general paradigm for compositional reasoning over structured knowledge.
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