title: Sketch-of-Thought: Efficient LLM Reasoning with Adaptive
    Cognitive-Inspired Sketching
summary: Recent advances in large language models have demonstrated remarkable
  reasoning capabilities through Chain of Thought (CoT) prompting, but often at
  the cost of excessive verbosity in their intermediate outputs, which increases
  computational overhead. We introduce Sketch-of-Thought (SoT), a novel prompting
  framework that combines cognitive-inspired reasoning paradigms with linguistic
  constraints to minimize token usage while preserving reasoning accuracy. SoT is
  designed as a flexible framework that can incorporate any custom reasoning
  paradigms based on cognitive science, and we instantiate it with three such
  paradigms - Conceptual Chaining, Chunked Symbolism, and Expert Lexicons - each
  tailored to different reasoning tasks and selected dynamically via a
  lightweight routing model. Through comprehensive evaluation across 15 reasoning
  datasets with multiple languages and multimodal scenarios, we demonstrate that
  SoT achieves token reductions of 76% with negligible accuracy impact. In
  certain domains like mathematical and multi-hop reasoning, it even improves
  accuracy while using significantly fewer tokens. Our code is publicly
  available: https://www.github.com/SimonAytes/SoT.
paper_authors._id: 67ce4bff5847e4787a7ebedd
paper_authors.name: Simon A. Aytes
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:01:10.363Z
paper_authors._id: 65f4060754ecda1ecb5797a0
paper_authors.avatarUrl: /avatars/f8b44524d36b505673cb538fd7895a82.svg
paper_authors.fullname: Simon Aytes
paper_authors.type: user
paper_authors.user: saytes
paper_authors._id: 67ce4bff5847e4787a7ebede
paper_authors.name: Jinheon Baek
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:01:13.328Z
paper_authors._id: 63036b6c5c70c21d0ea79d48
paper_authors.avatarUrl: /avatars/a7eb03f5cbd4eaa09fe807bbed8bc0f7.svg
paper_authors.fullname: Jinheon Baek
paper_authors.type: user
paper_authors.user: jinheon
paper_authors._id: 67ce4bff5847e4787a7ebedf
paper_authors.name: Sung Ju Hwang
paper_title: Sketch-of-Thought: Efficient LLM Reasoning with Adaptive
    Cognitive-Inspired Sketching
paper_summary: Recent advances in large language models have demonstrated remarkable
  reasoning capabilities through Chain of Thought (CoT) prompting, but often at
  the cost of excessive verbosity in their intermediate outputs, which increases
  computational overhead. We introduce Sketch-of-Thought (SoT), a novel prompting
  framework that combines cognitive-inspired reasoning paradigms with linguistic
  constraints to minimize token usage while preserving reasoning accuracy. SoT is
  designed as a flexible framework that can incorporate any custom reasoning
  paradigms based on cognitive science, and we instantiate it with three such
  paradigms - Conceptual Chaining, Chunked Symbolism, and Expert Lexicons - each
  tailored to different reasoning tasks and selected dynamically via a
  lightweight routing model. Through comprehensive evaluation across 15 reasoning
  datasets with multiple languages and multimodal scenarios, we demonstrate that
  SoT achieves token reductions of 76% with negligible accuracy impact. In
  certain domains like mathematical and multi-hop reasoning, it even improves
  accuracy while using significantly fewer tokens. Our code is publicly
  available: https://www.github.com/SimonAytes/SoT.
paper_ai_keywords: Sketch-of-Thought (SoT) Chain of Thought (CoT) token usage reasoning accuracy Conceptual Chaining Chunked Symbolism Expert Lexicons lightweight routing model multimodal scenarios reasoning datasets mathematical reasoning multi-hop reasoning