{"id":"adc7c58a-812f-4f1d-86b0-10129099d1af","arxiv_id":"2605.26397","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Empirical study using dual-persona rewrites shows LLMs produce more divergent autistic-persona outputs yet often collapse generations, with failure modes clustering by alignment strategy and systematic label reversals versus autistic human annotators.","lead":"The paper finds that safety-aligned LLMs rewrite autistic discourse differently depending on whether they adopt an autistic or neurotypical persona, with frequent collapse to identical outputs and qualitative failures like erasure and hallucination. A smart generalist might read it to understand how current AI alignment can embed neuronormative biases that affect representation of neurodiverse communication and resist simple prompt fixes.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Dual-persona prompts and sample selection may confound attribution of breakdown to alignment rather than prompt phrasing","rationale":"The reader's weakest_assumption correctly flags the experimental isolation problem as load-bearing for the causal claim about alignment training. The abstract provides no evidence that prompt phrasing was ablated or that sample selection was blinded to model training distributions, so the concern stands even after the full-text reference. No stronger internal inconsistency (e.g., quantitative vs. qualitative mismatch or scale vs. strategy contradiction) is visible from the given description.","tokens_in":1657,"tokens_out":359,"duration_ms":24340,"concrete_test":"Re-run the ten-model rewrite experiment on the same  discourse samples using three prompt variants per persona (original, synonym-swapped, and indirect-description versions); recompute lexical divergence, affective register shift, and cross-persona collapse rate. If the persona gap shrinks or disappears under variant prompts while semantic similarity remains matched, the effect is prompt-dependent.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that observed persona-specific divergence and collapse are caused by safety alignment's representational gap, not by the specific wording of the autistic/neurotypical persona instructions or by how the autistic discourse samples were chosen. The dual-persona rewrite paradigm uses distinct persona prompts whose lexical and framing differences could themselves modulate safety refusals or stylistic sanitization; without systematic ablation of prompt templates (e.g., replacing explicit \"autistic persona\" with indirect descriptors), the divergence metrics cannot isolate alignment effects. Likewise, \"naturally occurring\" samples risk selection bias if they overlap with training data patterns that alignment was tuned against. The paper reports clustering by alignment strategy, but this does not rule out prompt-by-alignment interactions.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that safety alignment in LLMs inadvertently encodes a sanitized, neuronormative representation of autistic communication. Using a dual-persona rewrite paradigm on naturally occurring autistic discourse, it prompts ten LLMs to rewrite from autistic or neurotypical personas, finding greater lexical/affective divergence in autistic-persona outputs despite semantic similarity, frequent cross-persona output collapse, and failure modes (erasure, stereotyped hallucination, meta-commentary) that cluster by alignment strategy. A multi-agent qualitative framework and comparison to autistic human annotators reveal label reversals, supporting the conclusion that alignment creates a representational gap unresolvable by prompt engineering.","tokens_in":1811,"tokens_out":585,"duration_ms":27483,"significance":"If the attribution to alignment holds after addressing confounds, the work would usefully document persona-specific fragility in aligned LLMs for neurodiverse communication, with the multi-agent qualitative framework and human-insider comparison providing a replicable lens beyond surface metrics. The clustering by alignment strategy rather than scale is a potentially falsifiable observation worth follow-up. However, without demonstrated isolation of alignment effects from prompt wording and sample selection, the significance for alignment research remains provisional.","major_comments":[{"comment":"Methods (implied by abstract): the abstract reports divergence metrics, collapse rates, and label reversals but provides no quantitative details on sample size, number of discourse samples, statistical tests, inter-annotator agreement for the qualitative analysis, or controls for prompt sensitivity; without these the central claims cannot be verified or reproduced.","section":"Methods"},{"comment":"Dual-persona rewrite paradigm (abstract and § on experimental setup): the design does not report ablation of persona prompt templates (e.g., replacing explicit 'autistic persona' phrasing with indirect descriptors) to test whether lexical/framing differences in the instructions themselves drive the observed divergence or collapse, leaving open the possibility that results reflect prompt-by-alignment interactions rather than alignment-induced representational gaps.","section":"Experimental setup"},{"comment":"Sample selection (abstract): 'naturally occurring' autistic discourse samples are not shown to have been screened for overlap with training data patterns that alignment tuning may have targeted, which risks confounding attribution of breakdown to safety alignment rather than dataset selection bias.","section":"Data"}],"minor_comments":[{"comment":"The multi-agent qualitative analysis framework is introduced but its agent roles, prompting protocol, and aggregation procedure are not detailed enough for independent replication.","section":"Qualitative analysis"},{"comment":"Figure or table presenting the clustering by alignment strategy should include the exact alignment categories used and the distance metric for clustering.","section":"Results"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive feedback, which highlights important areas for improving the transparency and robustness of our study. We address each major comment below, indicating revisions where appropriate.","responses":[{"response":"We agree with this assessment. The current manuscript version does not include these quantitative details in the abstract or methods summary. In the revised manuscript, we will provide full details on the number of discourse samples used, the statistical tests applied to the divergence metrics and collapse rates, inter-annotator agreement for the multi-agent qualitative analysis, and any sensitivity checks on prompt variations.","revision_made":"yes","referee_comment":"[Methods] Methods (implied by abstract): the abstract reports divergence metrics, collapse rates, and label reversals but provides no quantitative details on sample size, number of discourse samples, statistical tests, inter-annotator agreement for the qualitative analysis, or controls for prompt sensitivity; without these the central claims cannot be verified or reproduced."},{"response":"This is a fair point regarding potential confounds from prompt wording. Our study used explicit persona instructions as the core of the dual-persona paradigm, but we did not conduct ablations with indirect descriptors. We will revise to include a limitations discussion acknowledging this and explaining the rationale for explicit prompts, while noting that future work could explore indirect framings.","revision_made":"partial","referee_comment":"[Experimental setup] Dual-persona rewrite paradigm (abstract and § on experimental setup): the design does not report ablation of persona prompt templates (e.g., replacing explicit 'autistic persona' phrasing with indirect descriptors) to test whether lexical/framing differences in the instructions themselves drive the observed divergence or collapse, leaving open the possibility that results reflect prompt-by-alignment interactions rather than alignment-induced representational gaps."},{"response":"We recognize the risk of dataset selection bias. However, without access to the proprietary training data of the LLMs, comprehensive screening for overlap is not possible. We will add a dedicated limitations paragraph discussing this potential confound and its implications for attributing effects to alignment.","revision_made":"yes","referee_comment":"[Data] Sample selection (abstract): 'naturally occurring' autistic discourse samples are not shown to have been screened for overlap with training data patterns that alignment tuning may have targeted, which risks confounding attribution of breakdown to safety alignment rather than dataset selection bias."}],"tokens_in":1449,"tokens_out":519,"duration_ms":27862,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core observation is that ten LLMs produce rewrites of autistic discourse that diverge more in wording and tone under an autistic persona prompt than under a neurotypical one, with frequent collapse to near-identical outputs across personas. Human autistic annotators also reverse many of the models' labels. That pattern is the main empirical result.\n\nThe dual-persona rewrite plus multi-agent qualitative coding of failure modes (erasure, hallucination, evasion) is a fresh application to this domain. Clustering those modes by alignment strategy rather than model size is a useful descriptive step, and the direct human comparison adds a concrete check against model outputs.\n\nThe methods still leave room for doubt on causation. The autistic and neurotypical persona instructions differ in phrasing and framing, so lexical and safety-trigger differences could explain part of the divergence without needing to invoke a deep representational gap from alignment. The stress-test note on prompt ablation is on target here; without those controls the attribution stays suggestive. Sample choice for the source discourse also needs explicit checks against training-data overlap. The abstract gives no sample sizes, agreement stats, or prompt-sensitivity tests, so the full paper must supply those to make the quantitative claims stick.\n\nThis is for people working on fairness audits and accessibility tooling. A reader already running persona or bias tests will pick up the framework and the failure-mode taxonomy.\n\nIt is worth sending to referees. The setup is original enough and the practical stakes are real, even if the causal story needs tighter isolation of prompt effects.","headline":"The paper documents clear persona-driven output collapse and divergence in LLMs rewriting autistic text, but the link to alignment rests on prompts that may themselves drive the differences.","tokens_in":2312,"tokens_out":383,"would_cite":false,"duration_ms":21053,"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":"Safety alignment in LLMs encodes a sanitized, neuronormative view of autistic communication that prompt changes cannot fix.","keywords":["safety alignment","LLM bias","autistic communication","persona bias","generative breakdown","qualitative analysis","neuronormative representation","representational gap"],"falsifier":"An experiment in which prompt engineering or fine-tuning produces autistic-persona rewrites that maintain lexical and affective diversity matching human autistic annotator expectations without increasing harmful content.","tokens_in":2574,"feed_emoji":"","tokens_out":665,"duration_ms":28963,"temperature":0.7,"pith_summary":"The paper tests how safety training in large language models shapes their handling of autistic discourse by prompting ten models to rewrite real autistic texts from either an autistic or neurotypical persona. Autistic-persona outputs show larger shifts in wording and tone than neurotypical ones, even when core meaning stays the same, and many models simply produce the same text for both personas. A multi-agent qualitative review identifies repeated failures such as erasing unique elements, adding stereotypes, or dodging the task, patterns that group by alignment method rather than model size. Autistic human reviewers reverse many of the models' judgments, indicating the bias stems from training that favors a narrow norm.","feed_headline":"LLM safety alignment sanitizes autistic discourse","feed_subtitle":"Autistic personas trigger larger output changes and collapse than neurotypical ones, with human insiders reversing model labels.","key_machinery":"The dual-persona rewrite paradigm, which prompts LLMs to rewrite naturally occurring autistic discourse from either an autistic or neurotypical persona to measure divergence in form and register.","core_discovery":"Current alignment training causes persona-specific generative breakdown visible only through qualitative analysis, confirming a deep representational gap that prompt engineering cannot resolve. Models rewrite autistic discourse with greater lexical and affective divergence under an autistic persona, collapse cross-persona outputs to near-identical text, and exhibit failure modes of output erasure, stereotyped hallucination, and task-evasive meta-commentary that cluster by alignment strategy rather than scale. Targeted comparison with autistic human annotators shows systematic label reversals relative to LLM classifications.","pith_inferences":["The same alignment-induced sanitization may appear when models handle other neurodivergent or culturally specific communication styles.","Detection of such biases may require qualitative, multi-agent review methods in addition to standard quantitative benchmarks.","Alignment procedures that preserve persona diversity without sacrificing safety could address the representational gap identified here."],"forward_implications":["Autistic-persona rewrites diverge significantly more in lexical form and affective register than neurotypical rewrites despite equivalent semantic similarity.","Most models collapse cross-persona generations into near-identical outputs.","Systemic output erasure, stereotyped hallucination, and task-evasive meta-commentary are pervasive failure modes that cluster by alignment strategy.","Community-insider knowledge from autistic human annotators produces systematic label reversals relative to LLM classifications."],"fun_headline_variants":["Alignment erases autistic persona distinctions in LLMs","LLMs collapse autistic rewrites to identical outputs","Persona-specific breakdown clusters by alignment type","Human insiders reverse LLM classifications of autistic text"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The dual-persona rewrite paradigm and chosen autistic discourse samples isolate the effects of safety alignment on marginalized communication without confounding influences from prompt phrasing or dataset selection.","fun_headline_variants_meta":{"raw":{"variants":["Alignment erases autistic persona distinctions in LLMs","LLMs collapse autistic rewrites to identical outputs","Persona-specific breakdown clusters by alignment type","Human insiders reverse LLM classifications of autistic text"]},"model":"grok-4.3","cost_usd":0.005807,"raw_usage":{"total_tokens":2755,"prompt_tokens":649,"num_sources_used":0,"completion_tokens":54,"cost_in_usd_ticks":58074500,"prompt_tokens_details":{"text_tokens":649,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2052,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":649,"tokens_out":54,"duration_ms":21584,"temperature":1.0,"reasoning_tokens":2052,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T18:52:13.934988+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An experiment in which prompt engineering or fine-tuning produces autistic-persona rewrites that maintain lexical and affective diversity matching human autistic annotator expectations without increasing harmful content.","supporting_citations":[],"review_version":1}