LLM-Guided Monte Carlo Tree Search over Knowledge Graphs: Composing Mechanistic Explanations for Drug-Disease Pairs

2026-05-10

AI Summary

This work addresses the challenges of combinatorial explosion and long-range credit assignment in multi-hop reasoning over knowledge graphs for uncovering drug–disease mechanisms. The authors propose TESSERA, a novel framework that leverages large language models (LLMs) not as end-to-end generators but as local discriminators and state evaluators, integrating structural constraints from the knowledge graph with Monte Carlo Tree Search (MCTS) to enable neuro-symbolic, controllable reasoning. This approach generates interpretable, biologically plausible multi-step paths, demonstrating on two knowledge graphs its ability to both recover known mechanisms and identify alternative yet reasonable pathways. Ablation studies further validate the effectiveness of the dual-component LLM design.

Abstract

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.

Problem

• knowledge graphs
• compositional reasoning
• mechanistic explanations
• drug-disease pairs
• credit assignment

Innovation

• LLM-guided MCTS
• knowledge graph reasoning
• compositional explanation
• neuro-symbolic framework
• credit assignment