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arxiv: 2605.28305 · v1 · pith:G4O5FI7Bnew · submitted 2026-05-27 · 💻 cs.CL · cs.AI

Revisiting Anthropomorphic Reflection Markers in Large Language Model Reasoning

Yahan Yu , Noa Nakanishi , Fei Cheng This is my paper

Pith reviewed 2026-06-29 13:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords anthropomorphic markersreflection markersLLM reasoningmarker suppressionreasoning performancereflection behaviorlarge language models
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The pith

Suppressing anthropomorphic markers like 'wait' and 'hmm' does not harm and can improve LLM reasoning performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether explicit anthropomorphic markers are needed for good reasoning in large language models. Researchers suppress these markers using prompt and token interventions on four benchmarks with two model sizes. They find performance often stays the same or gets better when markers are removed, especially with bigger sampling budgets. Models still show reflection through marker-free verification after suppression. The markers appear to be optional surface features instead of essential parts of the reasoning process.

Core claim

Anthropomorphic reflection markers are not uniformly necessary for reasoning performance: suppressing them can preserve or improve performance in several settings, especially under larger sampling budgets. Marker suppression does not necessarily remove reflection behavior, as models can still perform marker-free verification. These results suggest that anthropomorphic markers tend to be surface cues rather than reliable proxies for reflection itself.

What carries the argument

Prompt-level and token-level interventions that suppress anthropomorphic reflection markers to isolate their role in LLM reasoning.

If this is right

  • Performance on reasoning tasks can be preserved or improved by removing anthropomorphic markers.
  • Larger sampling budgets benefit more from marker suppression.
  • Reflection continues without markers through alternative verification methods.
  • Research should explore reasoning mechanisms independent of explicit marker patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If markers are surface cues, training objectives that discourage them might yield more efficient models.
  • The findings could apply to reducing overthinking in LLM outputs on complex tasks.
  • Marker-free reflection suggests internal processes that could be studied through other output analysis methods.

Load-bearing premise

The prompt-level and token-level interventions remove only the target anthropomorphic markers without affecting other reasoning mechanisms.

What would settle it

Observing consistent performance degradation across all tested benchmarks and model scales when markers are suppressed would challenge the claim that suppression preserves or improves performance.

Figures

Figures reproduced from arXiv: 2605.28305 by Fei Cheng, Noa Nakanishi, Yahan Yu.

Figure 1
Figure 1. Figure 1: The overview of overthinking and the suppres [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An example prompt used for prompt suppres [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of explicit reflection behaviors and average response lengths. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Frequency of anthropomorphic markers in the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Pass@k results for larger k. Model Response To solve this problem, it’s best to define variables for each type of tree’s apples ... There must be a mistake in my understanding. Re￾reading the problem: ... But the initial answer given by the assistant was 170, which was incorrect because it didn’t account for the sister’s apples. So, the correct answer is 350 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An example of model response from the Token [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Word Frequency Shift on BIG-Bench Hard. wait let maybe perhaps alternatively hmm try consider 0 2 4 6 8 10 Frequency per 1000 words AIME 2024 No Sup. Prompt Sup. Token Sup. 1.5B 7B [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Word Frequency Shift on AIME 2024. let wait maybe break hmm alternatively okay again 0 2 4 6 8 10 Frequency per 1000 words GSM8K No Sup. Prompt Sup. Token Sup. 1.5B 7B [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Word Frequency Shift on GSM8K [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: An example prompt used for external LLM judge (Part One). [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: An example prompt used for external LLM judge (Part Two). [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Transition matrices for 1.5 and 7B models on GSM8K, BIG-Bench Hard, MMLU-Pro, and AIME 2024 [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that anthropomorphic reflection markers (e.g., 'wait', 'hmm', 'alternatively') in LLMs are surface cues rather than necessary components of reasoning or reflection. Using prompt-level instructions and token-level logit penalties to suppress these markers, experiments across four benchmarks and two model scales show that performance can be preserved or improved (especially at larger sampling budgets) and that marker-free verification remains possible.

Significance. If the central empirical findings hold after addressing intervention controls, the work would usefully challenge reliance on visible anthropomorphic markers as proxies for reflection, encouraging study of internal mechanisms instead. The dual intervention approach (prompt and token) and use of public benchmarks are positive features that support potential reproducibility.

major comments (2)
  1. [§3] §3 (Intervention Design): The prompt-level and token-level suppression methods are presented as isolating the effect of marker removal, yet no ablations with neutral interventions (e.g., unrelated meta-instructions or non-marker logit penalties) or internal probes (e.g., hidden-state analysis of verification traces) are reported. This is load-bearing for the claim that performance changes reflect marker absence rather than other behavioral shifts induced by the interventions.
  2. [§4] §4 (Results): The abstract asserts performance preservation or gains across four benchmarks and two scales 'especially under larger sampling budgets,' but the manuscript provides no quantitative tables, per-benchmark accuracies, error bars, statistical tests, or sample exclusion criteria. Without these, the magnitude and reliability of the reported effects cannot be assessed.
minor comments (2)
  1. [Introduction] Introduction: The definition and enumeration of 'anthropomorphic reflection markers' would benefit from explicit examples drawn from model generations to clarify the exact token set targeted by interventions.
  2. [§3] The manuscript does not discuss potential side effects of the token-level penalty on output length, diversity, or generation time, which could indirectly affect benchmark scores.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [§3] §3 (Intervention Design): The prompt-level and token-level suppression methods are presented as isolating the effect of marker removal, yet no ablations with neutral interventions (e.g., unrelated meta-instructions or non-marker logit penalties) or internal probes (e.g., hidden-state analysis of verification traces) are reported. This is load-bearing for the claim that performance changes reflect marker absence rather than other behavioral shifts induced by the interventions.

    Authors: We acknowledge that neutral-intervention ablations would strengthen causal isolation. Our design relies on two orthogonal methods (prompt instructions and targeted logit penalties) that converge on similar performance outcomes, which we interpret as evidence that effects track marker suppression rather than generic behavioral shifts. The token-level method applies penalties only to the specific marker tokens without broad meta-instructions. We did not conduct unrelated controls or hidden-state probes. In revision we will add an explicit limitations paragraph discussing these gaps and noting that internal-mechanism analysis remains future work. revision: partial

  2. Referee: [§4] §4 (Results): The abstract asserts performance preservation or gains across four benchmarks and two scales 'especially under larger sampling budgets,' but the manuscript provides no quantitative tables, per-benchmark accuracies, error bars, statistical tests, or sample exclusion criteria. Without these, the magnitude and reliability of the reported effects cannot be assessed.

    Authors: The referee correctly notes the absence of detailed quantitative reporting. We will expand the results section in the revised manuscript to include full per-benchmark accuracy tables, error bars, statistical tests, and explicit sample-size and exclusion criteria so that effect sizes and reliability can be evaluated directly. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical intervention study on public benchmarks

full rationale

The paper performs controlled experiments applying prompt-level instructions and token-level logit penalties to suppress specific anthropomorphic markers, then measures downstream accuracy on four standard benchmarks across two model scales. No equations, fitted parameters, or derivations are present; performance differences are reported as direct empirical outcomes rather than predictions derived from the interventions themselves. Claims about marker necessity rest on observable results under varying sampling budgets, not on self-referential definitions or self-citation chains. The work is self-contained against external benchmarks with no load-bearing self-citations or ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study with no mathematical derivations or new theoretical constructs. No free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.1-grok · 5696 in / 1031 out tokens · 31190 ms · 2026-06-29T13:11:23.996847+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

7 extracted references · 6 canonical work pages · 3 internal anchors

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