Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation

2026-01-25

AI Summary

This work addresses the limitations of large language models (LLMs) in open-ended investigative tasks, where constrained context windows and complex evidence dependencies hinder the generation of coherent and reliable global explanations. To overcome this, the authors propose the Explanation-on-Graph (EoG) framework, which formulates the task as abductive reasoning over a dependency graph. Within EoG, an LLM performs local evidence extraction and annotation, while a deterministic controller orchestrates graph traversal, state maintenance, and belief propagation to collaboratively construct a minimal explanation frontier. By decoupling semantic reasoning from control logic and integrating a graph-structured belief propagation mechanism, the framework enables dynamic evidence aggregation and autonomous hypothesis revision. Evaluated on the ITBench diagnostic benchmark, EoG achieves a seven-fold average improvement in Majority-at-k entity F1 over ReAct baselines, substantially enhancing explanation consistency and reliability.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

LLM agents excel when environments are mostly static and the needed information fits in a model's context window, but they often fail in open-ended investigations where explanations must be constructed by iteratively mining evidence from massive, heterogeneous operational data. These investigations exhibit hidden dependency structure: entities interact, signals co-vary, and the importance of a fact may only become clear after other evidence is discovered. Because the context window is bounded, agents must summarize intermediate findings before their significance is known, increasing the risk of discarding key evidence. ReAct-style agents are especially brittle in this regime. Their retrieve-summarize-reason loop makes conclusions sensitive to exploration order and introduces run-to-run non-determinism, producing a reliability gap where Pass-at-k may be high but Majority-at-k remains low. Simply sampling more rollouts or generating longer reasoning traces does not reliably stabilize results, since hypotheses cannot be autonomously checked as new evidence arrives and there is no explicit mechanism for belief bookkeeping and revision. In addition, ReAct entangles semantic reasoning with controller duties such as tool orchestration and state tracking, so execution errors and plan drift degrade reasoning while consuming scarce context. We address these issues by formulating investigation as abductive reasoning over a dependency graph and proposing EoG (Explanations over Graphs), a disaggregated framework in which an LLM performs bounded local evidence mining and labeling (cause vs symptom) while a deterministic controller manages traversal, state, and belief propagation to compute a minimal explanatory frontier. On a representative ITBench diagnostics task, EoG improves both accuracy and run-to-run consistency over ReAct baselines, including a 7x average gain in Majority-at-k entity F1.

Problem

• open-ended investigation
• context window limitation
• belief propagation
• reasoning consistency
• evidence mining

Innovation

• abductive reasoning
• dependency graph
• belief propagation
• disaggregated framework
• LLM agent