{"id":"a23604bb-605b-4704-9f76-43b165564035","arxiv_id":"2606.05864","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"LLMs do not exhibit the neglect-zero effect in structural priming tasks unlike humans.","lead":"The study tests whether LLMs show the neglect-zero effect, a human bias that ignores zero-models making propositions vacuously true. This comparison via structural priming could clarify differences between LLM and human language processing.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Structural priming paradigm may not isolate logical zero-model consideration independent of training data and prompt effects","rationale":"The reader's weakest assumption directly identifies the same load-bearing point. The abstract-only basis already correctly flags the paradigm's isolation as critical; without explicit controls for confounds in the full methods, the negative result remains provisional. No stronger internal inconsistency or measurement flaw is evident from the provided abstract and claim.","tokens_in":1641,"tokens_out":279,"duration_ms":17591,"concrete_test":"Re-run the prime-target pairs with lexical substitutions that preserve syntactic structure and zero-model semantics but minimize n-gram overlap with training data; if the no-effect result on targets disappears or reverses, the original finding is likely driven by surface statistics rather than logical model consideration.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (neglect-zero effect absent in the tested LLMs) depends on the structural priming setup successfully forcing consideration of zero-models via primes and detecting carry-over to targets. LLMs process via next-token prediction shaped by training distributions and surface prompt statistics; any observed lack of priming could therefore reflect failure of the prime to engage the intended logical structure rather than genuine absence of the bias. The abstract provides no detail on controls for lexical overlap, prompt formatting variants, or baseline priming strength in non-zero-model conditions.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript investigates whether LLMs exhibit the neglect-zero effect (human tendency to ignore zero-models that render propositions vacuously true via empty sets) by comparing behavior on two types of zero-model inferences against a non-zero-model inference. It employs a structural priming paradigm in which primes are constructed to force consideration of zero-models, then tests for carry-over facilitation to structurally similar targets; the reported outcome is that the neglect-zero effect appears absent in the LLMs examined.","tokens_in":1732,"tokens_out":396,"duration_ms":24770,"significance":"If the central claim is confirmed, the work would indicate a systematic divergence between LLM next-token processing and human logical inference on vacuous truths, with potential value for cognitive modeling and reasoning benchmarks. The public release of code at the cited GitHub repository is a clear strength that supports reproducibility.","major_comments":[{"comment":"Methods section: the structural priming design does not report explicit controls for lexical overlap, prompt-formatting variants, or baseline priming strength in non-zero-model conditions; without these, the observed lack of priming cannot be unambiguously attributed to absence of the neglect-zero bias rather than failure of the prime to engage the intended logical structure.","section":"Methods"},{"comment":"Results: the abstract (and by extension the reported evidence) provides only high-level summaries without data tables, error bars, statistical details, or per-model breakdowns, rendering it impossible to verify the strength or robustness of the claim that the effect is absent.","section":"Results"}],"minor_comments":[{"comment":"Abstract: notation for the neglect-zero effect and zero-models is introduced with LaTeX italics but without a concise operational definition that would allow readers to map the paradigm directly onto the logical property under test.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive feedback on our manuscript investigating the neglect-zero effect in LLMs. The comments highlight important aspects of methodological transparency and results presentation that we address below.","responses":[{"response":"We agree that the methods section would benefit from greater explicitness on these points. Our experimental materials were constructed with distinct lexical items between primes and targets to minimize overlap, multiple prompt phrasings were tested during piloting, and non-zero-model baseline conditions were included to establish priming strength. These elements were part of the design but not described in sufficient detail. We will revise the methods section to add a paragraph explicitly documenting the lexical controls, prompt variants, and baseline results, thereby strengthening the link between the observed lack of priming and the absence of the neglect-zero effect.","revision_made":"yes","referee_comment":"[Methods] Methods section: the structural priming design does not report explicit controls for lexical overlap, prompt-formatting variants, or baseline priming strength in non-zero-model conditions; without these, the observed lack of priming cannot be unambiguously attributed to absence of the neglect-zero bias rather than failure of the prime to engage the intended logical structure."},{"response":"The full results section contains per-model accuracy figures, statistical tests, and error bars within the figures. Nevertheless, we recognize that the abstract is high-level and that a consolidated table would improve verifiability. We will add a summary table reporting key metrics and statistics to the main text and revise the abstract to reference these quantitative details, allowing readers to assess the robustness of the claim directly.","revision_made":"yes","referee_comment":"[Results] Results: the abstract (and by extension the reported evidence) provides only high-level summaries without data tables, error bars, statistical details, or per-model breakdowns, rendering it impossible to verify the strength or robustness of the claim that the effect is absent."}],"tokens_in":1282,"tokens_out":414,"duration_ms":36243,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing to know is that this paper adapts structural priming to check whether LLMs ignore zero-models the way humans do in certain inferences, and the results point to the bias being absent in the models tested.\n\nWhat is new is the direct application of the priming setup to this specific logical bias. Earlier studies have examined other human biases in LLMs, but this one isolates zero-model inferences and prepares primes meant to force consideration of the empty set.\n\nThe paper does a couple of things cleanly. It releases the code, which lets others inspect the exact primes and targets. It also sets up a clear comparison between inferences that involve the neglect-zero effect and ones that do not.\n\nThe soft spots are in the evidence. The abstract stays at a high level with no numbers, tables, or description of controls for lexical overlap, prompt variants, or baseline priming strength. The stress-test concern lands: LLMs operate through next-token statistics, so a missing priming effect could simply mean the prime never engaged the intended logical structure rather than proving the bias is gone. Without seeing how they ruled out surface-level explanations, the central claim stays hard to evaluate.\n\nThis is for researchers who track whether LLMs mirror specific human logical biases. Someone working on cognitive modeling would get a targeted test to build on, provided the methods section expands. It deserves a serious referee because the idea is focused and the code is public, even if the current writeup needs more experimental detail before the conclusion can be trusted.","headline":"The paper tests neglect-zero in LLMs via structural priming and finds the effect absent, but the abstract gives almost no data or controls to back the claim.","tokens_in":2165,"tokens_out":380,"would_cite":false,"duration_ms":19642,"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":"Large language models do not exhibit the neglect-zero effect that humans show when reasoning about empty sets.","keywords":["neglect-zero effect","large language models","structural priming","zero-models","vacuous truth","logical inference","cognitive bias"],"falsifier":"A replication in which the same LLMs produce reliably faster or more probable responses on zero-model targets after zero-model primes, matching the human priming pattern, would contradict the reported result.","tokens_in":2559,"feed_emoji":"","tokens_out":604,"duration_ms":14339,"temperature":0.7,"pith_summary":"The paper tests whether LLMs display a human-like bias called the neglect-zero effect, in which reasoners ignore configurations that make a statement vacuously true because the relevant set is empty. Researchers apply a structural priming setup: they first present sentences that force attention to these zero-models, then measure whether that exposure changes how the model handles a follow-up inference that could also involve a zero-model. They compare this pattern against inferences that do not involve zero-models at all. The observed responses indicate that the LLMs continue to consider the zero-models even without the priming pressure that would be needed to overcome human neglect of them.","feed_headline":"LLMs do not neglect zero-models in reasoning tests","feed_subtitle":"Structural priming shows models keep considering empty-set cases that humans ignore by default.","key_machinery":"Structural priming paradigm that presents a prime sentence forcing consideration of a zero-model before a target sentence whose interpretation could also rest on a zero-model.","core_discovery":"Using structural priming, the study finds that the tested LLMs do not neglect zero-models in the manner humans do. Primes designed to highlight empty-set cases do not produce the facilitation pattern expected if the models were ignoring those cases on their own; instead, the models treat the zero-model inferences similarly to non-zero-model inferences throughout.","pith_inferences":["If the result generalizes, LLMs could serve as test subjects for logical reasoning that avoids certain human shortcuts.","Prompt engineering that works on humans by countering neglect may be unnecessary for these models.","The finding leaves open whether the same pattern appears in zero-shot settings without any priming sentences."],"forward_implications":["LLMs may treat vacuous truths as ordinary cases rather than defaulting to neglect.","Differences between LLM and human inference patterns appear even in tasks that rest on basic set emptiness.","The absence of the bias holds across the specific models and inference types examined in the experiments.","Training or architectural features may prevent the formation of the neglect-zero shortcut."],"fun_headline_variants":["No neglect-zero effect detected in LLMs","LLMs consider zero-models unlike humans","Priming study finds LLMs consider empty sets","LLMs treat zero-models same as other cases"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The structural priming setup cleanly reveals whether an LLM is already considering zero-models rather than being shaped by prompt wording or memorized training examples.","fun_headline_variants_meta":{"raw":{"variants":["No neglect-zero effect detected in LLMs","LLMs consider zero-models unlike humans","Priming study finds LLMs consider empty sets","LLMs treat zero-models same as other cases"]},"model":"grok-4.3","cost_usd":0.007401,"raw_usage":{"total_tokens":3379,"prompt_tokens":622,"num_sources_used":0,"completion_tokens":48,"cost_in_usd_ticks":74012000,"prompt_tokens_details":{"text_tokens":622,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2709,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":622,"tokens_out":48,"duration_ms":20127,"temperature":1.0,"reasoning_tokens":2709,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T01:34:44.062236+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A replication in which the same LLMs produce reliably faster or more probable responses on zero-model targets after zero-model primes, matching the human priming pattern, would contradict the reported result.","supporting_citations":[],"review_version":1}