{"id":"8b7be263-9a69-4eb7-9900-a9884af83bcd","arxiv_id":"2605.28305","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Suppressing anthropomorphic reflection markers via prompt and token interventions preserves or improves LLM reasoning performance on four benchmarks while models continue marker-free verification.","lead":"The paper tests whether removing human-like words such as 'wait' and 'hmm' from LLM reasoning traces changes task performance. Experiments across benchmarks show that suppressing these markers often preserves or improves accuracy while reflection behavior continues.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"Prompt/token interventions may alter reasoning mechanisms beyond marker removal","rationale":"The identified concern matches the reader's weakest_assumption exactly. The abstract provides no methods details on controls or measurement of marker-free reflection, so this remains the primary risk to the central claim even after full-text access.","tokens_in":1664,"tokens_out":266,"duration_ms":20003,"concrete_test":"Apply the same prompt-level and token-level methods but target a matched set of non-reflection tokens (e.g., common function words like 'the', 'and' with equivalent frequency); if performance shifts are comparable in magnitude and direction to the marker-suppression cases on the four benchmarks, the original effects are not marker-specific.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The claim that markers are surface cues (not necessary for performance or reflection) rests on interventions cleanly isolating marker suppression. Prompt-level instructions could induce new reasoning styles or reduce overthinking via meta-instruction rather than marker absence; token-level logit penalties could shift probability mass away from verification-related sequences in unmeasured ways. Without ablations comparing to neutral interventions or measuring internal reflection traces (e.g., via hidden-state probes), performance gains under larger budgets could be artifacts of the intervention itself rather than evidence against marker necessity.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","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.","tokens_in":1755,"tokens_out":506,"duration_ms":35173,"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":[{"comment":"§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.","section":"§3"},{"comment":"§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.","section":"§4"}],"minor_comments":[{"comment":"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.","section":"Introduction"},{"comment":"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.","section":"§3"}],"recommendation":"major_revision","confidential_remarks":"The absence of any quantitative results tables or detailed methods in the provided abstract and the low soundness score in the reader's note suggest the manuscript may be an early or incomplete draft; this should be verified against the full submission."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. We address each major comment below and indicate planned revisions.","responses":[{"response":"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_made":"partial","referee_comment":"[§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."},{"response":"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_made":"yes","referee_comment":"[§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."}],"tokens_in":1298,"tokens_out":402,"duration_ms":28029,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core finding is that prompt and token interventions to suppress markers such as 'wait', 'hmm', and 'alternatively' leave performance intact or better on reasoning tasks, especially with larger sampling, and models still verify without those words. This is new experimental work: the paper runs marker suppression on four benchmarks at two scales and reports the outcomes.\n\nThe experiments are straightforward and directly test necessity rather than just observing correlations. That gives a clear empirical angle on whether visible reflection markers are reliable proxies.\n\nThe main limitation is that the abstract gives no tables, error bars, or exclusion details, so the size of the effects and the exact intervention mechanics are not visible. The stress-test point about interventions possibly shifting other reasoning behaviors holds weight here; without ablations against neutral controls or internal probes, performance changes could partly reflect the intervention itself. The paper's claim that markers are surface cues rather than core mechanisms rests on the interventions being clean, which needs checking in the methods.\n\nThis is for readers working on LLM reasoning traces and interpretability of chain-of-thought. It adds targeted data to an existing line of work but does not introduce a new framework or large-scale result.\n\nIt deserves peer review because the experimental design is simple and falsifiable, even if the evidence needs more detail to stand on its own.","headline":"Suppression experiments show anthropomorphic markers like 'wait' are optional surface features, not required for reasoning or reflection.","tokens_in":2211,"tokens_out":336,"would_cite":false,"duration_ms":18405,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Suppressing anthropomorphic markers like 'wait' and 'hmm' does not harm and can improve LLM reasoning performance.","keywords":["anthropomorphic markers","reflection markers","LLM reasoning","marker suppression","reasoning performance","reflection behavior","large language models"],"falsifier":"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.","tokens_in":2561,"feed_emoji":"","tokens_out":521,"duration_ms":26845,"temperature":0.7,"pith_summary":"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.","feed_headline":"LLMs reason effectively without anthropomorphic markers","feed_subtitle":"Suppressing markers like 'wait' and 'hmm' maintains or boosts performance and allows marker-free verification.","key_machinery":"Prompt-level and token-level interventions that suppress anthropomorphic reflection markers to isolate their role in LLM reasoning.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["LLMs reflect without anthropomorphic markers","Suppressing markers maintains LLM performance","Reflection possible sans wait or hmm cues","Marker free verification works in LLMs","Anthropomorphic cues optional for reasoning"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The prompt-level and token-level interventions remove only the target anthropomorphic markers without affecting other reasoning mechanisms.","fun_headline_variants_meta":{"raw":{"variants":["LLMs reflect without anthropomorphic markers","Suppressing markers maintains LLM performance","Reflection possible sans wait or hmm cues","Marker free verification works in LLMs","Anthropomorphic cues optional for reasoning"]},"model":"grok-4.3","cost_usd":0.003874,"raw_usage":{"total_tokens":1959,"prompt_tokens":604,"num_sources_used":0,"completion_tokens":58,"cost_in_usd_ticks":38737000,"prompt_tokens_details":{"text_tokens":604,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1297,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":604,"tokens_out":58,"duration_ms":11071,"temperature":1.0,"reasoning_tokens":1297,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T13:11:23.996847+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"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.","supporting_citations":[],"review_version":1}