When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression
For researchers studying LLM reliability, this work offers a mechanistic understanding of why hallucinations occur, though it is incremental as it reframes known issues in graph terms without introducing new benchmarks or quantitative improvements.
The paper models next-token prediction in LLMs as graph search and identifies two mechanisms—Path Reuse and Path Compression—that cause reasoning hallucinations, providing a unified explanation for this phenomenon.
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.