HPFA: Hypergraph-Based Paired Failure Attribution for LLM Reasoning
A recent study published on arXiv (2608.02026) presents HPFA (Hypergraph-Based Paired Failure Attribution), a novel framework designed to improve the reasoning capabilities of large language models (LLMs) by pinpointing the underlying causes of their failures. This work addresses the challenge LLMs face in linking failures to specific reasoning steps, which hampers their reflective abilities. Existing approaches are either costly in computation or simplify reasoning paths into linear sequences. By employing hypergraphs to illustrate reasoning routes, HPFA efficiently identifies root causes. It utilizes a lightweight attributor model trained via supervised fine-tuning and reinforcement learning. Tests conducted on mathematical reasoning and coding tasks demonstrate HPFA's effectiveness. The paper, authored by researchers, is currently under peer review.
Key facts
- HPFA stands for Hypergraph-Based Paired Failure Attribution.
- The framework is designed for LLM reasoning failure attribution.
- It compares hyperedges of failure and success paths.
- It reduces search space for efficient root cause localization.
- It enables scalable synthesis of attribution data.
- A lightweight attributor model is trained via SFT and RL.
- Experiments were conducted on mathematical reasoning and agentic coding tasks.
- The paper is available on arXiv with ID 2608.02026.
Entities
Institutions
- arXiv