Stateful Reasoning via Insight Replay
Chain-of-Thought (CoT) reasoning has become a foundation for eliciting multi-step reasoning in large language models, but recent studies show that its benefits do not scale monotonically with chain length: while longer CoT generally enables a model to tackle harder problems, on a given problem, accuracy typically increases with CoT length up to a point, after which it declines. We identify a major cause of this phenomenon: as the CoT grows, the model's attention to critical insights produced earlier in the trace gradually weakens, making those insights progressively less accessible when they are most needed. Therefore, we propose \textbf{InsightReplay}, a stateful reasoning approach in wh...
| Authors | Bin Lei, Caiwen Ding, Jiachen Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 15 May 2026 |
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