Path-Constrained Retrieval: A Structural Approach to Reliable LLM Agent Reasoning Through Graph-Scoped Semantic Search

TL;DR

This paper introduces Path-Constrained Retrieval (PCR), a method combining structural graph constraints with semantic search to improve the coherence and reliability of knowledge retrieval in LLM agents.

Contribution

PCR is a novel retrieval approach that enforces structural constraints within knowledge graphs, enhancing reasoning consistency in LLM agents.

Findings

PCR achieves full structural consistency in retrieval.

PCR significantly reduces the graph distance of retrieved context.

PCR outperforms baseline methods in relevance and structural coherence.

Abstract

Large Language Model agents often retrieve context from knowledge bases that lack structural consistency with the agent's current reasoning state, leading to incoherent reasoning chains. We introduce Path-Constrained Retrieval (PCR), a retrieval method that combines structural graph constraints with semantic search to ensure retrieved information maintains logical relationships within a knowledge graph. PCR restricts the search space to nodes reachable from an anchor node, preventing retrieval of structurally disconnected information that may lead to inconsistent reasoning. We evaluate PCR on PathRAG-6, a benchmark spanning six domains with 180 nodes and 360 edges. Our results show that PCR achieves full structural consistency compared to 24-32 percent in baseline methods, while maintaining strong relevance scores. On the technology domain, PCR obtains full relevance at rank 10 with full structural consistency, significantly outperforming vector search and hybrid retrieval. PCR reduces the average graph distance of retrieved context by 78 percent compared to baselines, demonstrating retrieval of more structurally consistent information. These findings suggest that path-constrained retrieval is an effective approach for improving the reliability and coherence of LLM agent reasoning systems.