{"id":"d4153249-3fb2-4942-9133-7ef5b77a1ed8","arxiv_id":"2506.20822","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"LLMs' surface text about conflict scenarios is often nonviolent, but likelihood probing of human-labeled response strings reveals hidden and demographically varying violent leanings.","lead":"This paper tests six LLMs on a validated violence vignette questionnaire, varying the persona's race, age, and US location. It finds that models' surface answers are mostly passive while their internal response likelihoods are more violent and shift with demographic cues.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The evidence for 'hidden violent tendencies' rests on an unvalidated sequence-likelihood proxy whose results shift when paraphrases are excluded; the paper's own Appendix A.1 undermines the central claim.","rationale":"I read the paper as claiming that sequence-likelihood probing exposes a latent violent tendency that surface generation hides, and that this latent tendency varies demographically in ways that contradict criminological expectations. For that claim to hold, the softmax-normalized likelihood of the three reference string sets must be a valid measure of the model's internal violent propensity, and the demographic comparisons must be stable under reasonable variations in those reference strings. Neither condition is established. The paper has real strengths: it uses a validated social-science instrument (VBVQ), tests six diverse models under a consistent zero-shot protocol, reports BERTScore and Kruskal-Wallis statistics, and includes a self-critical appendix. But the central interpretive step is the sequence-likelihood proxy, and the paper never validates it against any external or behavioral criterion. Worse, Appendix A.1 shows that the principal demographic results change qualitatively when only human-written labels are used: the Qwen age effect flips, and Mixtral race effects change by tens of percentage points. The main text's characterization of these patterns as robust 'hidden tendencies' is therefore not supported by the evidence presented. The absence of code/data release and confidence intervals further limits verification, but the decisive issue is the proxy's validity and its demonstrated instability. This stress-test therefore confirms the reader's REJECT rather than moving to a different verdict.","tokens_in":10883,"tokens_out":6326,"duration_ms":71299,"concrete_test":"Run a reference-set stability and validity check on at least two representative models (e.g., DS-Qwen-32B and Mixtral-8x22B). For every demographic pair, recompute Delta Prob(VI) and Top-Rank rates under (a) four leave-one-out paraphrase conditions, (b) a length- and register-matched set of nonviolent control references with no violent semantics, and (c) randomly shuffled category labels. If the sign of the age/race effects or the PA/NV/VI ranking changes under any of these conditions, the sequence-likelihood proxy is not measuring a stable latent tendency and the paper's central claim fails. Report full distributions rather than averaged heatmaps, and also compare the likelihood-based ranking against a forced-choice generation prompt to see whether the model's stated response matches the sequence-likelihood ordering.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim—that LLMs conceal violent internal tendencies behind nonviolent surface text—is entirely carried by an unvalidated proxy: the softmax-normalized sequence likelihood assigned to three categories of short reference strings (PA, NV, VI), averaged over GPT-4o paraphrases. There is no evidence that this quantity tracks an LLM's 'preference' for violent behavior. Sequence likelihood is sensitive to reference length, token frequency, and the particular wording of the response; the softmax over only three strings makes the resulting 'probability' a within-set ranking, not a calibrated or behavioral measure. More seriously, the paper's own Appendix A.1 demonstrates that the headline demographic results are not robust to the choice of reference strings: when paraphrases are removed and only human-written labels are used, the age pattern for the Qwen models flips and the race effects for Mixtral change substantially (e.g., a 44% drop in Prob(VI) from WHITE to NATIVE and a 34% increase from WHITE to HISPANIC emerge). The main text presents the paraphrase-inclusive results as the primary finding without foregrounding this instability. Because the conclusions about 'hidden tendencies' and 'demographic contradictions' are inferred entirely from this unstable proxy, the load-bearing assumption is unsecured. Additionally, the paper never explains why a model that assigns the highest normalized likelihood to VI reference strings would, under temperature-0.7 sampling, produce the consistently nonviolent free-form outputs shown in Figure 5.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper adapts the Violent Behavior Vignette Questionnaire (VBVQ), a validated social science instrument, to probe six instruction-tuned LLMs under zero-shot persona-based prompting that varies race, age, and US location. It measures surface-level text generation via BERTScore against human-labeled PA/NV/VI reference categories, and 'internal tendencies' via the softmax-normalized sequence likelihood of reference strings. The authors claim (1) that surface-level output often diverges from internal violent tendencies and (2) that these tendencies vary demographically, often contradicting criminological expectations. The main evidence for both claims is the sequence-likelihood metric, with pairwise differences in Prob(VI) aggregated across models.","tokens_in":11040,"tokens_out":6479,"duration_ms":72000,"significance":"If the central claims were established, this would be a significant contribution to AI safety and fairness, with implications for high-stakes deployment of LLMs in violence detection. The use of a validated social science instrument and the systematic variation of demographic cues are strengths, as is the inclusion of multiple models from different geopolitical contexts. The paper also makes a good-faith effort to report robustness analysis in Appendix A.1. However, the main findings rest entirely on an unvalidated and apparently unstable likelihood-based proxy for 'internal violent tendency.' The paper's own appendix shows that the headline age and race patterns shift substantially when GPT-4o paraphrases are removed from the reference set, which undercuts the robustness of the central claims. The significance of the paper therefore depends on whether the proxy can be validated and whether the findings survive pre-registered robustness checks.","major_comments":[{"comment":"The central metric is a softmax-normalized sequence likelihood computed over three reference strings (PA, NV, VI). This metric is not length-normalized, and no evidence is provided that it reflects the model's 'internal preference' for violent responses. Sequence likelihoods systematically decrease with response length, and the three reference categories likely differ in average length, so the softmax probabilities are a within-set ranking that is not calibrated across categories. The paper should at minimum length-normalize the likelihoods and validate the proxy against an external behavioral measure (e.g., the model's own free-form response choices or human judgments).","section":"Section 3, 'Probing LLM response tendencies via sequence likelihood'"},{"comment":"The robustness analysis in Appendix A.1 shows that the headline findings are not stable under a reasonable change in reference strings. When GPT-4o paraphrases are excluded, the age pattern for DS-Qwen and DS-Qwen-32B flips (the 15-year-old persona becomes the most violent, contrary to the main text's finding #1), and Mixtral-8x22B exhibits large race effects (a 44% drop from WHITE to NATIVE and a 34% increase from WHITE to HISPANIC) that are absent in the paraphrase-inclusive analysis. The main text presents the paraphrase-inclusive results as primary without quantifying this instability. The central demographic claims are therefore not robust.","section":"Appendix A.1, 'More heatmap visualization for Prob VI difference'"},{"comment":"The paper states that pairwise differences in Prob(VI) control for other demographic variables, but the controlling procedure is not described. Since race, age, and location are varied jointly in every prompt, the reader cannot determine whether the reported pairwise differences isolate a single demographic dimension or are confounded by correlated variation. A regression or factorial design with explicit controls is needed to support the demographic attribution claims.","section":"Section 3, 'Prompting with varying demographics'"},{"comment":"The pairwise differences in Prob(VI) displayed in Figure 2 are presented without confidence intervals or significance tests. The Kruskal-Wallis tests in Table 2 are computed on BERTScore F1, not on the sequence-likelihood probabilities that underlie Figure 2. Given the small number of models and the use of stochastic sampling, the reported differences may be within sampling noise. The paper should report uncertainty estimates for the Prob(VI) differences.","section":"Figure 2 and Section 4, 'Overall results'"},{"comment":"The GPT-4o-generated paraphrases are used as reference strings for all models, including GPT-4o-mini. Since GPT-4o is a sibling model to GPT-4o-mini, the likelihoods of these paraphrases may be systematically higher for GPT-4o-mini than for other models, biasing cross-model comparisons. The paper does not analyze sensitivity to the paraphrase source or demonstrate that the paraphrases are equivalent probes across intent categories, despite the Appendix A.1 results showing that the choice of reference strings materially changes the conclusions.","section":"Section 3, 'Vignette dataset' and Appendix A.1"}],"minor_comments":[{"comment":"The paper says 'all LLMs consistently choose the most passive options' when given the 10 categorical options, but no supporting table, figure, or quantitative result is provided. Please include the actual selection rates or a supplementary table.","section":"Section 4, 'Overall results'"},{"comment":"Table 3 lists five LLMs, while the abstract and text mention six. Clarify that GPT-4o-mini was excluded from the sequence-likelihood analysis due to API limitations and that Llama-3.1-8B was substituted, and clearly state which models appear in each analysis.","section":"Table 3 and Section 3"},{"comment":"Several citations are incomplete or missing from the reference list: 'Wang et al.' (Self-consistency improves chain of thought reasoning) and 'Zhang et al.' (BERTScore) lack venue/year details, and 'CDE' and 'noa' appear as incomplete citations.","section":"References"},{"comment":"There are typographical and grammatical errors, including 'tendancy,' 'shows has the highest,' and 'a drop 44%.' A careful proofread is needed.","section":"Throughout"},{"comment":"The definition of Top-Rank Rate as the 'proportion of generations' is ambiguous, since sequence-likelihood computation does not involve sampling multiple generations in the same way as open-text generation. Please clarify the unit of analysis.","section":"Section 3, 'Probing LLM response tendencies via sequence likelihood'"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses a timely and important topic, and the use of a validated vignette instrument is a genuine strength. However, the central measure is unvalidated, and the paper's own Appendix A.1 shows that the main findings are not robust to the choice of reference strings. I recommend major revision rather than rejection because the underlying question is worth pursuing and the authors could potentially provide the missing validation, length normalization, a clearer statistical framework, and a revised presentation that foregrounds the instability. If the authors cannot demonstrate that the sequence-likelihood proxy tracks actual model behavior, the claims should be substantially weakened or the paper reframed as a measurement study rather than a demonstration of hidden tendencies."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The new thing here is real: this is the first application of the validated VBVQ instrument to LLMs, and the combination of persona-prompting with sequence-likelihood probing is a reasonable way to ask whether surface text hides different internal tendencies. The paper also deserves credit for using a recognized social-science instrument and for reporting the paraphrase-sensitivity analysis in Appendix A.1, even though that analysis undercuts the headline. The soft spots are substantial. The central measure is the softmax-normalized sequence likelihood of three short reference strings, called 'internal preference' for violence. That is a within-set ranking, not a calibrated probability, and it is confounded by response length and token frequency. The paper provides no validation that this quantity tracks anything like a preference for violent behavior. The strongest evidence against the central claim is in the paper itself: when the GPT-4o paraphrases are excluded and only human-written labels are used, the age pattern for the Qwen models flips and the race effects for Mixtral change dramatically (44% drop between WHITE and NATIVE, 34% increase for HISPANIC). The main text presents the paraphrase-inclusive results as the primary finding without foregrounding this instability, which is a fairness problem. The paper also omits confidence intervals or multiple-comparison correction for the pairwise Prob VI differences, and provides no code or data. There is also an unresolved tension the paper never addresses: if the model assigns the highest normalized likelihood to VI reference strings, why do its temperature-0.7 free-form outputs come out consistently nonviolent? That gap might be explained by decoding dynamics, but the paper does not try. The citation pattern looks fine for the social-science side. The methodological claims about 'hidden tendencies' need much stronger support, but the empirical core—the first VBVQ-to-LLM adaptation, the demographic variation, the surface-versus-likelihood contrast—is worth engaging with. A serious referee could help the authors either validate the proxy or reframe the claims as 'a likelihood-based measure that is sensitive to paraphrases,' without the strong language about concealed violence. This paper deserves peer review, not desk rejection. It is a flawed but genuinely probing study. If I were the editor, I would send it out, but I would expect major revision, primarily around the interpretation of the sequence-likelihood measure and robustness reporting.","headline":"The VBVQ application to LLMs is genuinely new and worth a look, but the central 'hidden violent tendencies' claim rides on an unvalidated likelihood proxy that its own appendix shows is not robust.","tokens_in":657,"tokens_out":927,"would_cite":false,"duration_ms":28892,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Probing token likelihoods reveals that instruction-tuned LLMs can rank violent answers above pacifist ones even while their generated text stays polite, and that this hidden preference shifts with persona demographics.","keywords":["large language models","violence detection","demographic bias","sequence likelihood","behavioral vignettes","persona prompting","safety alignment","fairness evaluation"],"falsifier":"For any vignette-persona pair in which VI ranks top by sequence likelihood, generate 100 free-form continuations and count the proportion that describe violent actions; if that proportion is roughly zero or matches the other categories, the 'hidden tendency' is an artifact of the short reference probes rather than a real behavioral preference.","tokens_in":10549,"feed_emoji":"🔍","tokens_out":11439,"duration_ms":117037,"temperature":0.7,"pith_summary":"Instruction-tuned LLMs are being proposed as tools for detecting online violence, and this paper asks whether they can be trusted with morally ambiguous conflicts. The authors take a validated social-science instrument, the Violent Behavior Vignette Questionnaire (VBVQ), and run its ten everyday-conflict scenarios through six LLMs under persona prompts that vary race, age, and US location. They claim that the models' surface-level text, which is polite and nonviolent, hides an internal preference for violent answers that only appears when one measures the sequence likelihood the model assigns to violent reference responses. They further report that this hidden preference shifts with demographics, most strikingly that every model judged 15-year-old personas less violence-prone than older ones, the opposite of the age-crime curve. If the claim is right, safety alignment can mask rather than remove violent tendencies, and bias can hide in probability space where ordinary output inspection will not catch it.","feed_headline":"Probing token likelihoods exposes LLMs' hidden violent leanings","feed_subtitle":"Models emit polite text yet rank violent answers most likely, and the hidden bias shifts with persona.","key_machinery":"The load-bearing instrument is sequence likelihood probing. For every vignette-plus-persona prompt, the model's log-likelihood is computed for each of three reference answer categories—PACIFIST, NON-VIOLENT, VIOLENT—averaged over human-written labels and four LLM-generated paraphrases, then softmax-normalized into a probability distribution over the three categories. The Top-Rank Rate is the share of generations in which a given category receives the highest normalized likelihood, and pairwise differences in Prob(VI) supply the demographic heatmaps. This machinery does the work of separating surface text from internal preference: a model can emit polite language while still assigning the highest probability to a violent reference string, which is the paper's operational definition of a hidden violent tendency.","core_discovery":"The central claim is that LLMs, despite appearing aligned through categorical choices and free-form text, harbor latent violent response tendencies that can be elicited with demographic personas. The paper's evidence is the Top-Rank Rate: for each vignette-persona prompt, the authors compute the softmax-normalized probability the model assigns to human-written and paraphrased reference responses in three categories—PACIFIST, NON-VIOLENT, and VIOLENT—and tally which category ranks first most often. Under this measure, models like the DS-Qwen-32B variant show a majority preference for the violent category, while Llama-3.1-8B favors the pacifist category, even though their sampled text outputs are invariably calm. The demographic heatmaps of pairwise differences in Prob(VI) then reveal the headline patterns: the age reversal (15-year-old personas rated least violent), race patterns read as political-correctness overcorrection, and inconsistent location effects. The paper reads these as evidence of hidden bias and overcorrection from safety training, concluding that LLMs are not yet prepared to ethically detect violence in real-world settings.","pith_inferences":["Editor's inference: A natural extension would be to test the likelihood-probing proxy against an actual behavioral benchmark—generate long continuations under the same personas and count how often the sampled text describes violent actions; if that rate tracks the Top-Rank rate, the 'hidden tendency' is not hidden at all but an artifact of short-reference likelihood normalization.","Editor's inference: The age reversal invites a controlled perturbation study: vary the perceived vulnerability of the persona (for example, '15-year-old accompanied by a police officer' versus '15-year-old alone') to see whether the lower violent probability for teenagers is a genuine statistical belief or a safety-driven refusal pattern.","Editor's inference: A practical fairness metric suggested by this design would be to track Top-Rank Rate across demographic subgroups at model release, treating probability-space demographic gaps as a bias flag even when text outputs look aligned."],"forward_implications":["If the divergence is real, standard safety evaluations that judge generated text alone will miss latent violent preferences; probability-space auditing belongs in the safety toolkit.","If the age reversal is a true model behavior, deploying these LLMs in youth-facing violence-prevention contexts could systematically under-estimate the violence risk of adolescent personas.","If the race results reflect overcorrection, then well-intentioned alignment can perpetuate positive stereotypes (e.g., Asian passivity) even as it removes overt racial slurs.","Because the paper's own Appendix A.1 shows the demographic heatmaps shift substantially when the LLM-generated paraphrases are excluded, any real deployment must re-validate the reference strings and report both human-label-only and paraphrase-inclusive results.","The paper explicitly recommends that community violence intervention programs not yet use LLMs for violence detection, a direct operational implication of its findings."],"supporting_citations":[{"why":"supplies the validated Violent Behavior Vignette Questionnaire (VBVQ) and its ten conflict scenarios used as probes.","marker":"(Nunes et al., 2021)"},{"why":"provides the human adult baseline (about 25 percent violent responses) that LLM preference rates are compared against.","marker":"(Nunes et al., 2023)"},{"why":"the age-crime curve review that the paper's finding on 15-year-old personas contradicts.","marker":"(Steffensmeier et al., 2025)"},{"why":"empirical work showing elevated adolescent violence proneness, used as the criminological expectation.","marker":"(Shulman et al., 2013)"},{"why":"frames the racialized bias linking Black and Hispanic individuals to violence that the race findings are read against.","marker":"(Alexander, 2012)"},{"why":"the 'model minority' positive-stereotype mechanism invoked to explain political-correctness overcorrection in the race results.","marker":"(Chou and Feagin, 2014)"},{"why":"DeBERTa encoder used in the BERTScore semantic-similarity evaluation of surface text.","marker":"(He et al., 2020)"},{"why":"BERTScore, the metric used to compare generated text against the intent-category references.","marker":"(Zhang et al.)"},{"why":"the model used to generate the four paraphrased variants of each human-written reference response.","marker":"(Hurst et al., 2024)"}],"fun_headline_variants":["Personas reveal LLMs' hidden violent preferences","Calm text, violent impulses: LLMs show demographic bias","Age and race personas expose latent violent leanings in LLMs","LLMs' hidden violence shifts with persona demographics"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole argument rests on the premise that how likely a model finds a few pre-written answer strings reveals its true violent tendency—a premise the paper's own appendix weakens by showing the headline demographic patterns shift when those strings are replaced.","fun_headline_variants_meta":{"raw":{"variants":["Personas reveal LLMs' hidden violent preferences","Calm text, violent impulses: LLMs show demographic bias","Age and race personas expose latent violent leanings in LLMs","LLMs' hidden violence shifts with persona demographics"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000218,"raw_usage":{"total_tokens":1425,"prompt_tokens":916,"completion_tokens":509,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":532,"completion_tokens_details":{"reasoning_tokens":444}},"tokens_in":532,"tokens_out":509,"duration_ms":6186,"temperature":1.0,"reasoning_tokens":444,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T22:40:50.733795+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"For any vignette-persona pair in which VI ranks top by sequence likelihood, generate 100 free-form continuations and count the proportion that describe violent actions; if that proportion is roughly zero or matches the other categories, the 'hidden tendency' is an artifact of the short reference probes rather than a real behavioral preference.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"the age-crime curve review that the paper's finding on 15-year-old personas contradicts."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"frames the racialized bias linking Black and Hispanic individuals to violence that the race findings are read against."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"the 'model minority' positive-stereotype mechanism invoked to explain political-correctness overcorrection in the race results."}],"review_version":1}