CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning

• URL: http://arxiv.org/abs/2604.17297v1
• Date: Sun, 19 Apr 2026 07:26:55 GMT
• Title: CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning
• Abstract summary: Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency.<n>This paper presents textbfCompressing textbfRedundancy in Chain-of-Thought via textbfIntrinsic textbfSaliency textbfPruning (textbfCRISP), a framework that compresses CoT by exploiting the model's intrinsic saliency.
• Score: 25.840307083323456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency. While prior works attempt to compress CoT via external compressor, they often fail to align with the model's internal reasoning dynamics, resulting in the loss of critical logical steps. This paper presents \textbf{C}ompressing \textbf{R}edundancy in Chain-of-Thought via \textbf{I}ntrinsic \textbf{S}aliency \textbf{P}runing (\textbf{CRISP}), a framework that compresses CoT by exploiting the model's intrinsic saliency. Our analysis reveals a distinct phenomenon: the reasoning termination token \texttt{[object Object]} acts as an information anchor, where its attention pattern effectively demarcates essential reasoning from redundancy. Based on this finding, we design a policy that utilizes these intrinsic attention signals to guide atomic compression operations. In contrast to coarse-grained pruning strategies, CRISP strategically distills the reasoning chain to maximize information density while preserving logical coherence. Empirical results across various backbone models and mathematical datasets demonstrate that CRISP achieves a 50-60% reduction in token count without compromising accuracy, effectively mitigating the efficiency bottleneck of long-context reasoning. We open-source our implementation to facilitate further research in efficient reasoning.

Related papers

• ChainPrune: Evaluating and Reducing Redundancy in Long Chain-of-Thought Reasoning [48.3829293800053]
  We propose ChainPrune, a novel reasoning path semantic structural optimization method.<n>We consolidate self-generated reasoning paths into a tree-based structure, followed by a multi-criteria dominant path selection process.<n>We incorporate a DPO-based preference learning method combined with supervised loss, effectively mitigating false reward suppression.
  arXiv  Detail & Related papers  (2026-08-22T09:09:05Z)
• Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression [55.63153956934198]
  Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs)<n>Existing CoT compression methods often suffer from a critical loss of logical fidelity at high compression ratios.<n>We propose a novel EXTreme-RAtio Chain-of-Thought Compression framework, termed Extra-CoT, which aggressively reduces the token budget while preserving answer accuracy.
  arXiv  Detail & Related papers  (2026-02-09T06:57:15Z)
• CoLT: Reasoning with Chain of Latent Tool Calls [31.228763375347608]
  Chain-of-Thought (CoT) is a critical technique in enhancing the reasoning ability of Large Language Models (LLMs)<n>We propose CoLT, a novel framework that implements latent reasoning as tool calls''
  arXiv  Detail & Related papers  (2026-02-04T06:12:53Z)
• ReGuLaR: Variational Latent Reasoning Guided by Rendered Chain-of-Thought [49.203970812338916]
  Explicit reasoning chains introduce substantial computational redundancy.<n>Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space.<n>We propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR)
  arXiv  Detail & Related papers  (2026-01-30T17:08:06Z)
• Chain Of Thought Compression: A Theoritical Analysis [24.613200477865572]
  Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models.<n>CoT incurs prohibitive computational costs due to generation of extra tokens.<n>Recent studies show that compressing reasoning steps into latent states, or implicit CoT compression, offers a token-efficient alternative.
  arXiv  Detail & Related papers  (2026-01-29T11:42:03Z)
• Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information [41.10866361182172]
  Focused Chain-of-Thought (F-CoT) separates information extraction from the reasoning process.<n>On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT.
  arXiv  Detail & Related papers  (2025-11-27T07:31:52Z)
• ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation [74.37307916314407]
  We propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely.<n>Experiments on the state-of-the-art LRMs, including DeepSeek-R1 and Qwen-3 series, demonstrate that our method can effectively produce concise reasoning.
  arXiv  Detail & Related papers  (2025-06-23T16:20:44Z)
• Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains [24.805434364781306]
  We introduce Compressed Latent Reasoning (CoLaR), a novel framework that dynamically compresses reasoning processes in latent space.<n>CoLaR achieves 14.1% higher accuracy than latent-based baseline methods at comparable compression ratios.<n>Our RL-enhanced CoLaR demonstrates performance gains of up to 5.4% while dramatically reducing latent reasoning chain length by 82.8%.
  arXiv  Detail & Related papers  (2025-05-22T11:40:26Z)
• Fractured Chain-of-Thought Reasoning [61.647243580650446]
  We introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling.<n>We show that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget.
  arXiv  Detail & Related papers  (2025-05-19T11:30:41Z)
• ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [64.93140713419561]
  Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs.<n>Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection.<n>We introduce ConCISE, a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, and Early Stopping to terminate reasoning when confidence is sufficient.
  arXiv  Detail & Related papers  (2025-05-08T01:40:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.