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Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

Research Efficiency & Systems

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Overall 79
Content 95
Popularity 41

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Merged summary

TL;DR - Memory-Augmented Compression retrieves reusable reasoning summaries as prefill context, enabling LLMs to produce shorter chain-of-thought traces without sacrificing as much accuracy. The training-free method improves reasoning performance over Chain-of-Draft while delivering 1.14–1.49× lower latency than standard CoT.

  • Formalizes a “Context-Generation Substitution Law,” framing explicit context as a substitute for some decode-time reasoning generation.
  • Builds memories from historical traces that capture reusable patterns, constraints, and critical operations rather than storing raw demonstrations.
  • Improves over Chain-of-Draft by 21.4 points on GSM8K, 28.0 on MATH, 29.5 on BBH, and 6.61 on MMLU-Sci.
  • Works with token-level, trace-level, and inference-state compression; analyses attribute gains to memory relevance rather than context length alone.

Sources (1)

Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning

arXiv cs.CL Simeng Zhang, Yilong Chen, Wenyuan Zhang, Zhenyu Zhang, Yao Chen, Junyuan Shang, Tingwen Liu 2026-08-21 arXiv:2608.21265
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-04 14:16:19.096512 UTC

TL;DR - Memory-Augmented Compression retrieves reusable reasoning summaries as prefill context, enabling LLMs to produce shorter chain-of-thought traces without sacrificing as much accuracy. The training-free method improves reasoning performance over Chain-of-Draft while delivering 1.14–1.49× lower latency than standard CoT.

  • Formalizes a “Context-Generation Substitution Law,” framing explicit context as a substitute for some decode-time reasoning generation.
  • Builds memories from historical traces that capture reusable patterns, constraints, and critical operations rather than storing raw demonstrations.
  • Improves over Chain-of-Draft by 21.4 points on GSM8K, 28.0 on MATH, 29.5 on BBH, and 6.61 on MMLU-Sci.
  • Works with token-level, trace-level, and inference-state compression; analyses attribute gains to memory relevance rather than context length alone.
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