mlsanityData to 7 Oct 2026

Revisiting Anthropomorphic Reflection Markers in Large Language Model Reasoning

arXiv 2605.28305 ↗First posted 27 May 2026cs.CL cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large Language Models (LLMs) often produce explicit reflective traces during complex reasoning, accompanied by anthropomorphic markers such as wait, hmm, and alternatively. Although these markers are commonly used as visible indicators of reflection, their mechanisms remain unclear, which leaves the risk of overthinking associated with redundant and repetitive reflection markers. In this work, we revisit anthropomorphic reflection markers, examining their necessity for reasoning and role in the reflection. We suppress these markers through prompt-level and token-level interventions, and analyze their effects on task performance across four benchmarks and two model scales. Our results show that anthropomorphic markers are not uniformly necessary for reasoning performance: suppressing them can preserve or improve performance in several settings, especially under larger sampling budgets. Meanwhile, marker suppression does not necessarily remove reflection behavior, as models can still perform marker-free verification. These suggest that anthropomorphic markers tend to be surface cues rather than reliable proxies for reflection itself, and motivate future research on reasoning mechanisms beyond explicit marker patterns.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 14 of its 37 references that are in the map. First posted on arXiv 27 May 2026.

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  1. 2024Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking14 Mar

Other references · 13

  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,193
  4. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,057
  5. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 946
  6. 2025OpenAI GPT-5 System Cardcited by 910
  7. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  8. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMscited by 564
  9. 2025Chain of Draft: Thinking Faster by Writing Lesscited by 290
  10. 2025Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiencycited by 65
  11. 2025Understanding Aha Moments: from External Observations to Internal Mechanismscited by 48
  12. 2024On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Modelscited by 42
1 more
  1. 2025Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Modelscited by 23

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  2. 2025Illusions of reflection: open-ended task reveals systematic failures in Large Language Models' reflective reasoning
  3. 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
  4. 2025The Mirror Loop: Recursive Non-Convergence in Generative Reasoning Systems
  5. 2025From Emergence to Control: Probing and Modulating Self-Reflection in Language Models
  6. 2026Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
  7. 2026PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
  8. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks