When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression

TL;DR

This paper models reasoning hallucinations in large language models as arising from path reuse and compression in a graph-based framework, explaining how memorized knowledge and training dynamics lead to unsupported conclusions.

Contribution

It introduces a graph perspective to understand hallucinations, identifying path reuse and compression as key mechanisms in their emergence.

Findings

Hallucinations result from path reuse during early training.

Path compression causes multi-step paths to become shortcut edges.

The graph model explains various behaviors in downstream applications.

Abstract

Reasoning hallucinations in large language models (LLMs) often appear as fluent yet unsupported conclusions that violate either the given context or underlying factual knowledge. Although such failures are widely observed, the mechanisms by which decoder-only Transformers produce them remain poorly understood. We model next-token prediction as a graph search process over an underlying graph, where entities correspond to nodes and learned transitions form edges. From this perspective, contextual reasoning is a constrained search over a sampled subgraph (intrinsic reasoning), while context-free queries rely on memorized structures in the underlying graph (extrinsic reasoning). We show that reasoning hallucinations arise from two fundamental mechanisms: \textbf{Path Reuse}, where memorized knowledge overrides contextual constraints during early training, and \textbf{Path Compression}, where frequently traversed multi-step paths collapse into shortcut edges in later training. Together, these mechanisms provide a unified explanation for reasoning hallucinations in LLMs and connected to well-known behaviors observed in downstream applications.