arXiv AI
Aug 5
Soft Guidance Starts to Outperform CoT Prompting as LLMs Improve
arXiv:2608. 03550v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting remains the standard baseline for evaluating models' reasoning abilities.
Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
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arXiv AI
Jun 4
Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression
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.
arXiv Computer Vision
5d ago
Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation
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 AI
Jul 28
What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation
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 Computation and Language
Sep 1
Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression
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 AI
Jul 24
Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers
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.
arXiv Machine Learning
Jun 15
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
arXiv:2602. 08324v5 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference.
arXiv Computation and Language
Aug 28
Boosting LLM Exploration via Weak-Model Guidance in RLVR
The paper introduces a method to enhance large language model (LLM) exploration in Reinforcement Learning with Verifiable Rewards (RLVR) by guiding the target model with partial reasoning trajectories from smaller, weaker language models. This weak-model guidance disrupts over‑confidence, preserves generative diversity, and mitigates entropy collapse without extra fine‑tuning or complex reward designs. Experiments on mathematical benchmarks show consistent improvements over vanilla RLVR, especially as the number of allowed attempts ($k$) increases, indicating broader reasoning coverage.
arXiv AI
Jul 22
Robust Reasoning Benchmark
arXiv:2604. 08571v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on the context and textual formatting.
arXiv Computation and Language
Sep 10
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
The paper evaluates entropy-based pruning for compressing Chain-of-Thought (CoT) reasoning in large models. Across multiple models and tasks, low- and high-entropy step selection shows no advantage over random pruning, and low-entropy token retention only helps on mathematical benchmarks due to the low entropy of numeric tokens. Patching a few CoT tokens with their original activations restores near-perfect performance, indicating that task information is distributed throughout the entire reasoning chain rather than concentrated in a small set of tokens.
arXiv AI
Jun 30
Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning
arXiv:2508. 02178v3 Announce Type: replace Abstract: Large reasoning models (LRMs) often exhibit overthinking, producing verbose Chain-of-Thought (CoT) traces that increase inference cost and obscure the underlying reasoning process.
arXiv Machine Learning
Oct 23
AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency
AtManRL introduces differentiable attention manipulation to train salient chain-of-thought reasoning in large language models. By optimizing an additive attention mask that identifies the tokens necessary to recover the model’s answer, the method derives a saliency reward combined with an outcome reward in a GRPO framework with decoupled advantages. Across three models and four benchmarks, AtManRL improves saliency and faithfulness metrics while maintaining comparable accuracy to length‑penalty baselines.