mlsanityData to 7 Oct 2026

Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information

arXiv 2511.22176 ↗First posted 27 Nov 2025cs.CL cs.AIcited by 1

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we propose a training-free, input-centric approach. Inspired by cognitive psychology, we introduce Focused Chain-of-Thought (F-CoT), which separates information extraction from the reasoning process. F-CoT first organizes the essential information from a query into a concise, structured context and then guides the model to reason exclusively over this context. By preventing attention to irrelevant details, F-CoT naturally produces shorter reasoning paths. On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT. These results highlight structured input as a simple yet effective lever for more efficient LLM reasoning.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 39 of its 61 references that are in the map. First posted on arXiv 27 Nov 2025.

Builds on

39 of 61 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Chain of Draft: Thinking Faster by Writing Less25 Feb
  2. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models6 May
  3. 2022Large Language Models are Zero-Shot Reasoners24 May
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 35

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  6. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  7. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  8. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  9. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  10. 2025s1: Simple test-time scalingcited by 1,311
  11. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  12. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
23 more
  1. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  2. 2022PAL: Program-aided Language Modelscited by 635
  3. 2025L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learningcited by 381
  4. 2025Demystifying Long Chain-of-Thought Reasoning in LLMscited by 323
  5. 2025TokenSkip: Controllable Chain-of-Thought Compression in LLMscited by 246
  6. 2025Training Language Models to Reason Efficientlycited by 242
  7. 2023Proving Test Set Contamination in Black Box Language Modelscited by 227
  8. 2023Time Travel in LLMs: Tracing Data Contamination in Large Language Modelscited by 175
  9. 2025The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Taskscited by 150
  10. 2024Concise Thoughts: Impact of Output Length on LLM Reasoning and Costcited by 108
  11. 2025How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approachcited by 95
  12. 2024What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineeringcited by 88
  13. 2025Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinkingcited by 46
  14. 2025Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Modelscited by 37
  15. 2025Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunitiescited by 30
  16. 2024Over-Reasoning and Redundant Calculation of Large Language Modelscited by 27
  17. 2024Can LLMs Solve longer Math Word Problems Better?cited by 22
  18. 2025On the Emergence of Thinking in LLMs I: Searching for the Right Intuitioncited by 22
  19. 2025Not All Tokens Are What You Need In Thinkingcited by 20
  20. 2024Combining Knowledge Graphs and Large Language Modelscited by 16
  21. 2025LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scalingcited by 15
  22. 2025Adaptive GoGI-Skip: Coupling Goal-Gradient Importance with Dynamic Uncertainty for Efficient Reasoningcited by 12
  23. 2022Improving Task Generalization via Unified Schema Promptcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoningcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Hierarchical Chain-of-Thought: Enhancing LLM Reasoning Performance and Efficiency
  2. 2025Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
  3. 2026A State-Transition Framework for Efficient LLM Reasoning
  4. 2024Understanding Chain-of-Thought in LLMs through Information Theory
  5. 2026When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning
  6. 2024Extending Token Computation for LLM Reasoning
  7. 2026D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models
  8. 2024On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models