Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608. 03550v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting remains the standard baseline for evaluating models' reasoning abilities.
arXiv:2510. 08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance.
The paper introduces Difficulty-Aware Chain-of-Thought (DA‑CoT), a training‑free framework that selectively applies chain‑of‑thought prompting in reasoning segmentation tasks based on the entropy of attention maps. Low entropy signals confident, localized grounding, while high entropy indicates uncertainty and the potential benefit of additional reasoning. DA‑CoT also incorporates a Bidirectional Rethinking mechanism to automatically reverse reasoning decisions when initial outputs fail, thereby improving robustness and inference efficiency while achieving state‑of‑the‑art results on Reasoning Segmentation benchmarks.
arXiv:2605. 26795v2 Announce Type: replace Abstract: Chain-of-thought (CoT) prompting enhances large language model performance, yet what drives these gains remains unclear.
arXiv:2608.31066v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves multi-step problem solving, but long reasoning traces inflate inference cost. Token-level CoT compression red...
arXiv:2603. 01437v2 Announce Type: replace Abstract: As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning.