mlsanityData to 7 Oct 2026

ReEfBench: Quantifying the Reasoning Efficiency of LLMs

arXiv 2601.03550 ↗First posted 7 Jan 2026cs.AIcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscures whether performance gains stem from genuine reasoning or mere verbosity. To address this, (1) we propose a novel neuro-symbolic framework for the non-intrusive, comprehensive process-centric evaluation of reasoning. (2) Through this lens, we identify four distinct behavioral prototypes and diagnose the failure modes. (3) We examine the impact of inference mode, training strategy, and model scale. Our analysis reveals that extended token generation is not a prerequisite for deep reasoning. Furthermore, we reveal critical constraints: mixing long and short CoT data in training risks in premature saturation and collapse, while distillation into smaller models captures behavioral length but fails to replicate logical efficacy due to intrinsic capacity limits.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 17 of its 34 references that are in the map. First posted on arXiv 7 Jan 2026.

Builds on

17 of 34 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025LogiNumSynth: Synthesizing Joint Logical-Numerical Reasoning Problems for Language Models13 Oct
  2. 2025Adaptive Selection of Symbolic Languages for Improving LLM Logical Reasoning12 Oct
  3. 2025Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study5 Jun
  4. 2025Qwen3 Technical Report14 May
  5. 2025ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning3 Feb

Other references · 12

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  2. 2024Qwen2.5 Technical Reportcited by 4,713
  3. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  4. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 298
  5. 2025Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learningcited by 260
  6. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?cited by 215
  7. 2025Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Modelscited by 37
  8. 2024EVOLvE: Evaluating and Optimizing LLMs For In-Context Explorationcited by 33
  9. 2025Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insightscited by 30
  10. 2025How to Correctly Report LLM-as-a-Judge Evaluationscited by 25
  11. 2025Revisiting Overthinking in Long Chain-of-Thought from the Perspective of Self-Doubtcited by 17
  12. 2025Evaluating the Systematic Reasoning Abilities of Large Language Models through Graph Coloringcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Metacognition in LLMs: Foundations, Progress, and Opportunitiescited by 1
  2. 2026TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problemscited by 0
  3. 2026The Reasoning Tax: Token Economics of LLM Reasoning Across Task Types and Deployment Contextscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
  2. 2025Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
  3. 2025Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM
  4. 2026Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
  5. 2023Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance
  6. 2024Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus
  7. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  8. 2023Large Language Models Are Not Strong Abstract Reasoners