Title: Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
Primary area: interpretability and explainable AI
Keywords: large language models, chain-of-thought, reasoning hop generalization, mechanistic interpretability

Abstract: Chain-of-thought (CoT) reasoning has become the standard paradigm for enabling Large Language Models (LLMs) to solve complex problems.
However, recent studies reveal a sharp performance drop in *reasoning hop generalization* scenarios, where the required number of reasoning steps exceeds training distributions while the underlying algorithm remains unchanged. 
The internal mechanisms driving this failure remain poorly understood.
In this work, we conduct a systematic study tasks from multiple domains, and find that errors concentrate at token positions of a few critical error types, rather than being uniformly distributed. 
Closer inspection reveals that these token-level erroneous predictions stem from internal *competition mechanisms*: certain attention heads, termed *erroneous processing heads* (ep heads), tip the balance by amplifying incorrect reasoning trajectories while suppressing correct ones. 
Notably, removing individual ep heads during inference can often restore the correct predictions.
Motivated by these insights, we propose test-time correction of reasoning, a lightweight intervention method that dynamically identifies and deactivates ep heads in the reasoning process. 
Extensive experiments across different tasks and LLMs show that it consistently improves reasoning hop generalization, highlighting both its effectiveness and potential.

TL;DR: This work sets out to explore the patterns and mechanisms for reasoning hop generalization errors, and secondly leverages these insights to develop strategies for correcting these errors.