{"id":"191366b4-8072-4ec2-b9df-f98d86226d8d","arxiv_id":"2501.08985","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"In simulations, agents with critical or calm-nervous traits persuaded most often, and persuasion ability was non-transitive across personality pairs.","lead":"Six AI agents with different personality profiles were made to argue about six misinformation topics, and the authors counted how often each one changed the other's mind. The study claims that calm, non-aggressive styles persuade best and that influence ranks are non-transitive, but it only tests simulated agents, not real people.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Tables 1–3 report dozens of events per agent-pair/topic while the Methods section states only 90 total interactions; the reported success rates and non-transitivity rest on undefined, arithmetically impossible counts.","rationale":"The reader's verdict of REJECT is well placed, but I locate the decisive problem differently. The reader's stated weakest_assumption is that the six prompted LLM agents are valid analogues of human Big Five personality traits. That is a genuine limitation, and the Discussion does overclaim human-facing implications. However, it is a modeling-validity concern that could in principle be debated through machine-behavior or prompt-fidelity arguments. The paper's own tables present a harder, objective problem: the counts cannot be reconciled with the stated 90-interaction protocol. This is not a matter of external validity or disciplinary consensus; it is internally inconsistent evidence. Consequently, the percentages and the non-transitivity result are unsupported regardless of how one resolves the proxy question. An honest stress-test should press this first because it is checkable from the manuscript alone. I therefore agree with the rejection but disagree with the reader's identification of the single most load-bearing assumption. The reader's rationale does mention internal table inconsistencies, but the formally stated weakest_assumption points elsewhere. The recommended verdict remains unchanged because the rejection is already justified; my concern does not move the verdict, it reinforces it with a more concrete and decisive defect.","tokens_in":6574,"tokens_out":4126,"duration_ms":43281,"concrete_test":"Ask the authors for the raw AgentScope logs for one cell, e.g., Agent 4 vs. Agent 5 on HIV. From those logs, count the interactions under the paper's own definition (one outcome per interaction). The row should contain exactly one nonzero outcome, not 64; if the authors instead counted conversational turns or repeated trials, the total must sum to the 90 stated interactions. Recompute the 59.4% rate and the non-transitivity ordering from the reconciled counts. If the log shows 64 distinct episodes, then the experimental design must be restated; if no log is available, the headline results are unverified.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claims—critical traits persuade at 59.4%, non-aggressive strategies exceed 40%, and persuasion is non-transitive—all depend on the counts in Tables 1–3. Those counts are internally impossible under the stated protocol. The Methods section says that six agents engaged in pairwise discussions, yielding '15 unique agent pair combinations per topic and 90 interactions across all topics,' and defines each interaction as ending in exactly one of four outcomes. Yet Table 1's first row (Agent 4 vs. Agent 5, HIV) reports 38 + 14 + 10 + 2 = 64 outcomes for a single pair-topic, and Table 1 alone sums to 326 rows across its six rows. Tables 2 and 3 are similarly impossible, with rows summing to 74, 59, 55, etc. If the counts are conversational turns or sub-episodes rather than interactions, then the percentages labeled 'success rate' are not success rates over the 90 interactions, and the abstract's comparison is uninterpretable. Since no raw logs, prompts, code, or trial counts are released, there is no way to recover what was actually counted. The non-transitivity result (Agent 6 > Agent 4 > Agent 5, but Agent 5 > Agent 6) is derived from these same counts, so it cannot be accepted until the counting unit is specified and reconciled with the 90-interaction design. The unvalidated LLM-as-human proxy is also a concern, but the arithmetic inconsistency is more fundamental: even granting the proxy, the reported evidence cannot support the claim.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper reports a multi-agent simulation in which six LLM agents, each assigned one of three Big Five personality dimensions (Extraversion, Agreeableness, Neuroticism) at opposite poles, hold pairwise discussions on six misinformation topics. The stated design yields 15 pairs per topic and 90 total interactions, with each interaction classified into one of four outcomes. The authors claim that critical-analytical traits achieve a 59.4% success rate in HIV discussions, that non-aggressive strategies maintain success rates above 40%, and that persuasion effectiveness is non-transitive. They draw implications for personality-aware misinformation interventions. The central quantitative claims, however, depend on counts in Tables 1-3 that are arithmetically inconsistent with the stated 90-interaction protocol, and the paper provides no experimental details (trial counts, temperatures, classification rules) or statistical tests to support the reported rates.","tokens_in":1792,"tokens_out":2938,"duration_ms":66801,"significance":"The research question is timely: understanding whether personality profiles systematically shape persuasion in misinformation contexts could inform real-world intervention design, and the use of multi-agent LLM simulation is a plausible methodological direction. The paper also raises an interesting hypothesis that non-aggressive, rapport-building styles may outperform confrontational correction, and the non-transitivity idea is worth probing with proper data. That said, the significance of the paper as a contribution is currently unsupported: the headline numbers cannot be reconstructed from the stated protocol, the non-transitivity conclusion is read off aggregate percentages without any ordering rule or significance testing, and the agents are prompted to embody traits and then treated as valid analogues of human personality. If the empirical foundation were repaired, the topic could be of interest to a computational social science or AI-behavior audience, but in its present form the contribution is not credible.","major_comments":[{"comment":"The stated protocol says there are 15 unique agent pairs per topic and 90 interactions across all topics, with each interaction ending in exactly one of four outcomes. Yet Table 1's HIV row reports 38+14+10+2=64 outcomes for a single pair-topic, and Table 1 alone sums to 326 counts. Table 2 and Table 3 rows also show sums of 74, 59, 55, etc., far exceeding the 90 total interactions. If the counts are conversational turns, sub-episodes, or repeated trials, then the percentages labeled 'success rates' are not success rates over the 90 interactions as defined. The abstract's headline figures (59.4%, >40%, non-transitivity) therefore have no clear denominator. No raw logs, prompts, code, or trial counts are provided, so the reader cannot recover what was actually counted. This arithmetic inconsistency invalidates every reported rate in the paper.","section":"Methodology - Experiment Settings; Tables 1-3"},{"comment":"The non-transitivity conclusion is derived by comparing aggregate percentages across Tables 1-3 without specifying the ordering criterion (e.g., mean success rate, majority cycle) and without any significance test. Since the denominators in the three tables differ and are inconsistent with the stated 90-interaction design, the claim 'Agent 6 > Agent 4 > Agent 5 but Agent 5 > Agent 6' is not a demonstrated empirical finding. Even if the counts were correct, the absence of repeated trials and variance estimates would make the apparent intransitivity indistinguishable from sampling noise. The conclusion 'transitivity is not satisfied' is therefore unsupported.","section":"Results, paragraph beginning 'Based on the first two experiments'"},{"comment":"The paper gives no details of the fine-tuning or prompting procedure, and it provides no validation that the trait-labeled agents actually behave like humans with the corresponding Big Five traits. The Discussion frames the results in human terms ('individuals with critical personality traits typically demonstrate...'), but the only evidence is that agents given different trait labels produced different text. This design is self-referential: agents are constructed to embody the traits, and the study then reports that traits shape persuasion. Without a validation study or a clear statement that conclusions apply only to prompted LLMs, the human-facing interpretation in the Discussion is not warranted.","section":"Methodology, 'The large language model was fine-tuned to embody specific personality traits'"},{"comment":"The manuscript omits essential reproducibility details: the number of independent runs per pair-topic, the sampling temperature, random seeds, the exact outcome-classification rubric (and whether a human or automated judge assigned the four outcomes), and any inter-rater agreement or classifier accuracy measure. No code, data, or appendix is released. These omissions make it impossible to check the reported percentages or to assess whether the outcome classification was reliable and consistent across the thousands of counts the tables imply.","section":"Methodology, 'For each interaction, the number of times the four scenarios occurred and their frequency were recorded'"}],"minor_comments":[{"comment":"The text and figure captions report inconsistent numbers: for Agent 1 versus Agent 2, Agent 4, and Agent 6, the text gives success rates of 0.475, 0.422, and 0.424, while Figure 1's caption says 47.5%, 33.2%, and 40.4%; the failure and draw rates also disagree. Please reconcile the text and figures.","section":"Results, 'Effectiveness of Non-Aggressive Persuasion Strategies'; Figures 1 and 2"},{"comment":"The column headings 'Agent 5 vs. Agent 6' and 'Agent 6 vs. Agent 5' do not clearly indicate which column is the number of times Agent 5 convinced Agent 6 versus the reverse. In addition, many rows in Tables 1-3 do not sum to 100% (e.g., Table 3, 5G row sums to 88.6%), indicating either rounding errors or more substantial tabulation mistakes.","section":"Table 3"},{"comment":"The text contains numerous typographical and copyediting errors (e.g., 'represent-ing', 'Pevious', 'V oracek', 'Len-Domnguez'), and some references appear corrupted (e.g., Bastick's journal name and volume). A careful proofread is needed.","section":"Throughout"},{"comment":"The abbreviation 'Chloride' for the fluoride topic is confusing; the topic is about fluoride, so 'Fluoride' would be a clearer abbreviation.","section":"Methodology, list of misinformation topics"},{"comment":"The reference list includes entries such as FORCE11's FAIR Data principles and Gebru et al.'s Datasheets for Datasets that are not cited in the body text. These should either be integrated into a relevant discussion (e.g., data availability) or removed.","section":"Related Work / References"}],"recommendation":"reject","confidential_remarks":"The paper's central quantitative claims are undermined by the counting inconsistency in Tables 1-3, which cannot be resolved from the manuscript: the stated 90-interaction design is contradicted by over 300 counts in Table 1 alone. This is not a local error but a load-bearing flaw affecting the abstract's headline rates, the non-transitivity argument, and the practical implications. Even if the authors clarified the counting unit, the lack of statistical testing and the unvalidated LLM-as-human-proxy assumption would remain serious problems. The manuscript also appears to be a rough draft with omitted experimental details and several unreferenced citations. I recommend rejection, though I would be open to a substantially rewritten and methodologically rigorous revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe one thing to know: this paper's central numbers cannot be right. The Methods says six agents, 15 pairs per topic, 6 topics, 90 interactions, each interaction ending in exactly one of four outcomes. Table 1 alone has rows summing to 64, 57, 60, 55, 45, and 45 for a single pair-topic. So either the tables count something other than interactions, or the protocol description is wrong. Either way, the percentages in the abstract (59.4%, above 40%, non-transitivity) are arithmetic artifacts of undefined counts.\n\nThe idea is not without merit. Pairwise LLM persuasion with Big Five-inspired personas is a natural extension of prior work on personality and fake news. The non-transitive pattern (Agent 6 > Agent 4 > Agent 5, but Agent 5 > Agent 6) would be worth probing if the measurements were sound. The paper also engages the relevant literature and is upfront about omitting Conscientiousness and Openness. That is honest.\n\nBut the soft spots go beyond the count inconsistency. There are no trial counts, no random seeds, no temperature settings, no outcome-classification protocol (who decides when a conversation ends in 'bilateral influence'?), no statistical tests, and no released prompts, code, or logs. Even with consistent counts, the entire edifice rests on an unvalidated proxy: six GLM-4-Flash agents prompted to be 'sympathetic/cooperative' or 'moody/nervous' are treated as valid stand-ins for human personality-driven persuasion. The Discussion then draws practical conclusions for real-world intervention design. That is a leap the data cannot support. The circularity is real but secondary: prompted traits will of course shape outputs; that tells us about the prompt system, not human psychology.\n\nVerdict: reject. This is not a case where a few missing details can be patched; the reported evidence is internally impossible, and the human-facing conclusions are overclaimed. The research question deserves a proper study with a defined counting unit, multiple seeds, a pre-specified outcome classifier, and a baseline. As written, I would not send this to a serious referee.","headline":"Interesting setup undone by internally impossible counts: Tables 1–3 cannot hold under the paper's own 90-interaction protocol, so the non-transitivity and non-aggressive-success claims are not supported.","tokens_in":7394,"tokens_out":3553,"would_cite":false,"duration_ms":33049,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that personality-prompted AI agents, debating misinformation in pairs, show critical traits win evidence-based arguments, non-aggressive styles keep persuasion above 40%, and overall persuasion rankings are non-transitive.","keywords":["misinformation","Big Five personality","AI agents","persuasion","agent-based modeling","non-transitive influence","personality-aware intervention"],"falsifier":"A human replication study: recruit participants with questionnaire-measured Big Five traits to hold the same six pairwise misinformation discussions and compare whether the 59.4% HIV success rate and the non-transitive ordering reproduce; if human persuasion is transitive or rates differ materially, the agent results are artifacts of prompt phrasing.","tokens_in":6310,"feed_emoji":"🧠","tokens_out":7814,"duration_ms":73800,"temperature":0.7,"pith_summary":"This paper tries to establish that personality traits, modeled as prompt-level Big Five profiles in AI agents, predict who convinces whom in one-on-one misinformation discussions. Using six agents spanning Extraversion, Agreeableness, and Neuroticism, the authors ran 90 pair-topic combinations across six misinformation topics and counted four outcomes: one side persuades the other, mutual resistance, or bilateral influence. They report that a critical, analytical agent reached a 59.4% success rate against a nervous/sensitive agent on HIV misinformation, that sympathetic or non-assertive agents sustained persuasion rates above 40% across partners, and that persuasion effectiveness is non-transitive—agent A can beat B, B beat C, yet C beat A. If these patterns hold for human conversations, misinformation interventions should be personality-aware and favor trust-building, emotional connection, and low-pressure dialogue over direct confrontation. The intended contribution is a computational method for testing personality-based persuasion dynamics before deploying real-world countermeasures.","feed_headline":"Critical personality agent hits 59.4% success in HIV myth debates","feed_subtitle":"Pairwise chatbot debates show non-aggressive persuasion holds above 40%, and no personality ranks on top.","key_machinery":"The central machinery is a pairwise persuasion trial: two trait-labeled large-language-model agents exchange turns on a misinformation topic, and the transcript is classified into one of four outcomes—A persuades B, B persuades A, mutual resistance, or bilateral influence. Frequencies of these outcomes per pair and topic are the data from which success rates and the non-transitive ranking are read. The trait labels are drawn from three Big Five dimensions: Extraversion (bold/energetic versus shy/bashful), Agreeableness (sympathetic/cooperative versus cold/harsh), and Neuroticism (moody/nervous versus relaxed/calm).","core_discovery":"The paper's central claim is that personality combinations, instantiated as trait-labeled AI agents, systematically shape who persuades whom in misinformation dialogues. In the authors' experiments, six agents—bold/energetic, shy/bashful, sympathetic/cooperative, cold/harsh, moody/nervous, and relaxed/calm—were paired across six misinformation topics. The cold/harsh ('critical') agent persuaded the moody/nervous agent in 59.4% of HIV-related trials, while the sympathetic and non-assertive agents sustained success rates above 40% against multiple partners. The authors also report a non-transitive pattern: the relaxed/calm agent out-persuades the critical agent, the critical agent out-persuades the nervous agent, yet the nervous agent out-persuades the relaxed/calm agent on topics such as MMR. From this they conclude that effective misinformation correction should be personality-aware and should prioritize emotional connection and trust-building over confrontational argument.","pith_inferences":["Editorial inference: non-transitivity implies influence is a property of the pair, not the individual; aggregating 'persuasiveness' scores across targets would mislead intervention design.","Editorial inference: the 59.4% and above-40% figures are likely sensitive to prompt wording and model choice; a natural extension is to vary the trait prompt and the underlying language model to test stability.","Editorial inference: the same pairwise design could be adapted to test whether message framing alone—warm versus confrontational wording without personality labels—reproduces the persuasion rates, separating social-trait effects from pure text-style effects."],"forward_implications":["Interventions for evidence-based misinformation such as HIV myths may be most effective when delivered by a critical, analytical communicator to a neurotic or sensitive audience.","Rapport-building, non-confrontational styles can sustain persuasion above 40% regardless of partner personality, making trust-based correction a robust default strategy.","Because persuasion rankings are non-transitive, there is no universally best persuader; the optimal messenger depends on the target's personality combination.","Agent-based simulation can cheaply pre-screen personality-matched message strategies before fielding them against real misinformation."],"supporting_citations":[{"why":"Supplies the empirical link between Big Five traits and susceptibility to misinformation that motivates which traits the agents embody.","marker":"Calvillo et al. 2021"},{"why":"Provides the meta-analytic baseline for personality correlates of conspiracy beliefs used to design agent trait combinations.","marker":"Goreis and Voracek 2019"},{"why":"Supports the trait-to-behavior mapping (agreeable and conscientious users verify before sharing) underlying the agents' persuasion assumptions.","marker":"Sampat and Raj 2022b"},{"why":"Establishes machine behavior as a lens for studying AI agents as social actors, justifying the agent-based method.","marker":"Rahwan et al. 2019"},{"why":"Shows that large-language-model agents can fact-check news, the prior result this paper extends to personality-driven persuasion.","marker":"Li, Zhang, and Malthouse 2024"},{"why":"Demonstrates multi-agent simulation of fake-news spread, providing the baseline agent-based modeling approach.","marker":"Małecki and Puścian 2022"}],"fun_headline_variants":["Critical AI's 59.4% win rate in HIV myth debates","Non-aggressive AI persuasion stays above 40% in tests","Personality AI reveals non-transitive persuasion pattern","Agent-based study: critical traits persuade nervous agents"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that a language model prompted with a trait label such as 'moody/nervous' reasons and persuades the way a human who has that trait does, so the measured rates describe human misinformation dynamics rather than just the prompt system.","fun_headline_variants_meta":{"raw":{"variants":["Critical AI's 59.4% win rate in HIV myth debates","Non-aggressive AI persuasion stays above 40% in tests","Personality AI reveals non-transitive persuasion pattern","Agent-based study: critical traits persuade nervous agents"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000341,"raw_usage":{"total_tokens":1899,"prompt_tokens":985,"completion_tokens":914,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":601,"completion_tokens_details":{"reasoning_tokens":847}},"tokens_in":601,"tokens_out":914,"duration_ms":9114,"temperature":1.0,"reasoning_tokens":847,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T20:12:56.242028+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A human replication study: recruit participants with questionnaire-measured Big Five traits to hold the same six pairwise misinformation discussions and compare whether the 59.4% HIV success rate and the non-transitive ordering reproduce; if human persuasion is transitive or rates differ materially, the agent results are artifacts of prompt phrasing.","supporting_citations":[{"cited_title":"W.; Christakis, N","cited_arxiv_id":null,"evidence_quote":"Establishes machine behavior as a lens for studying AI agents as social actors, justifying the agent-based method."},{"cited_title":"P.; Garcia, R","cited_arxiv_id":null,"evidence_quote":"Supplies the empirical link between Big Five traits and susceptibility to misinformation that motivates which traits the agents embody."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the meta-analytic baseline for personality correlates of conspiracy beliefs used to design agent trait combinations."}],"review_version":1}