{"id":"41c50c94-dae7-4cea-834f-c1dd964eea00","arxiv_id":"2508.05625","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"Linear probes trained on LLM hidden states can detect when and how persuasion happens in multi-turn conversations, matching or beating prompting at lower cost.","lead":"This paper uses small linear probes, simple classifiers that read the hidden activity of large language models, to detect persuasion events in multi-turn conversations. If the probes work as claimed, they offer a cheap way to study manipulation, deception, and negotiation at scale.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Body text is encoding-corrupted and carries a watermark for arXiv:2508.05615v2 [cs.CV], not the claimed 2508.05625 (cs.CL), so the label-provenance and probe-vs-prompting evidence for the central claim is unverifiable.","rationale":"The reader already set UNVERDICTED at low confidence, and my stress-test confirms that is the only defensible verdict. The strongest claim is empirical: probes localize persuasion points and match/beat prompting. Empirical claims require inspectable methods and results. Here the entire body is garbled and the text carries a watermark for a different arXiv ID, which blocks every downstream check—label validity, probe training, evaluation, comparison. This is a missing-support/omitted-proof concern, not a disagreement with consensus. The reader's weakest assumption about label ground truth is exactly the part we cannot evaluate; my concern is broader because it includes the very existence of readable methods. I am not alleging fraud; corruption or extraction failure explains the artifact. Regardless, until a clean copy is available, the concern is load-bearing because it prevents verification. I therefore recommend no change to the reader's UNVERDICTED verdict.","tokens_in":15282,"tokens_out":3601,"duration_ms":38101,"concrete_test":"Fetch the arXiv source/PDF directly for arXiv:2508.05625, not the pasted text, and grep for '05615' and for the label-construction paragraph (e.g., keywords like 'per-turn', 'persuadee', 'human annotation'). If the source contains the foreign watermark or lacks a readable label-definition and probe-training section, request a clean copy from the authors. If no clean copy with externally defined ground-truth labels is produced, the persuasion-localization claim remains unverified.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The supplied full text is not a readable manuscript: it consists mostly of replacement-character mojibake, and it explicitly embeds 'arXiv:2508.05615v2 [cs.CV] 13 Nov 2025' near the top, which does not match the claimed ID. This is in-scope evidence of missing support: there is no inspectable methods section, no description of how per-turn persuasion labels were constructed, and no probe-vs-prompting evaluation protocol. The abstract's central claim—that linear probes 'can identify the point in a conversation where the persuadee was persuaded' and can outperform prompting—rests on exactly these details. If the persuasion-turn labels are derived from thresholding the probe's own scores, or are LLM-generated rather than externally defined, the headline finding is a fitted construction, not a discovery. With the document in this state, neither the reader nor anyone else can distinguish these possibilities. This is not an assertion of misconduct; a corrupted upload or extraction artifact may explain it. But as a scientific argument, the paper is currently unverifiable because its evidence is inaccessible, and the foreign arXiv watermark compounds rather than resolves that problem.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes training linear probes on LLM hidden states to study persuasion dynamics in multi-turn conversations, claiming to capture three aspects—persuasion success, persuadee personality, and persuasion strategy—at both the sample and dataset levels. The headline claims are that probes can identify the turn at which a persuadee is persuaded and that probes match or outperform expensive prompting-based methods, particularly for strategy detection. However, the supplied full text is almost entirely unreadable replacement-character mojibake and is internally labeled as arXiv:2508.05615v2 [cs.CV], not the claimed arXiv:2508.05625 (cs.CL). As a result, the methods, label-construction protocol, evaluation details, tables, and figures cannot be inspected, and the paper's central claims are unverifiable in its current form.","tokens_in":15573,"tokens_out":3891,"duration_ms":41099,"significance":"If the claims were substantiated, the work would be a useful, low-cost interpretability tool for studying persuasion and potentially other complex social behaviors in long conversations. The combination of linear probes with cognitive-science-derived targets is a reasonable and interesting direction. However, the submitted manuscript does not provide inspectable evidence for any of these claims: there are no probe accuracies, no chance baselines, no description of the labels, no validation protocol, and no comparison methodology. The paper's potential significance cannot compensate for the fact that, as submitted, the argument cannot be assessed.","major_comments":[{"comment":"The supplied manuscript is mostly replacement-character mojibake and explicitly embeds the line 'arXiv:2508.05615v2 [cs.CV] 13 Nov 2025', which does not match the claimed paper ID or subject class. This is not a presentation issue: the methods, equations, tables, and figures needed to evaluate the central claims are inaccessible. I cannot verify how the probes were trained, what the persuasion labels are, how the persuasion point was defined, or how the probe-versus-prompting comparison was conducted.","section":"Full text, first page"},{"comment":"The central claim that probes 'can identify the point in a conversation where the persuadee was persuaded' requires an independent, per-turn ground truth. The abstract reports no label source, no validation protocol, and no definition of the persuasion point. If the point is defined by thresholding the probe's own output scores, or if the labels are LLM-generated without external validation, then the claimed 'identification' is a fitted construction rather than a discovery. The unreadable full text provides no evidence to rule out these possibilities.","section":"Abstract, 'persuasion point' claim"},{"comment":"The further claim that probes 'can identify ... where persuasive success generally occurs across the entire dataset' is also underspecified. It is not stated how the dataset-level point is aggregated from turn-level scores or whether it is compared to an external behavioral or annotation-based benchmark. Without such a specification, the result may reduce to an artifact of the probe score distribution.","section":"Abstract, dataset-level localization claim"},{"comment":"The claim that probes 'do just as well and even outperform prompting in some settings' is unsupported in the visible text. No task accuracies, chance baselines, model and prompt details, dataset sizes, error bars, significance tests, or cost measurements are reported. Since this comparison is one of the two main contributions asserted in the abstract, the omission is load-bearing.","section":"Abstract, probe-versus-prompting comparison"}],"minor_comments":[{"comment":"If a corrected manuscript is provided, it should explicitly define the term 'persuasion point' and identify the source and annotation protocol for the per-turn persuasion labels, even in the abstract.","section":"General"},{"comment":"A reproducibility statement listing the exact LLM versions, datasets, licenses, and code/artifact links would be needed to make the proposed method usable and checkable.","section":"General"}],"recommendation":"reject","confidential_remarks":"The submitted full text is not usable as a manuscript: it is largely unreadable and carries a different arXiv identifier (2508.05615v2 [cs.CV]) from the claimed one. This may be a file corruption or extraction artifact rather than a deliberate issue, but it makes scientific review impossible. The editor may wish to verify the source file and consider asking the authors to resubmit a clean version before any further review."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's the short version: the abstract promises a genuinely interesting and testable contribution—linear probes that localize when persuasion succeeds in multi-turn dialogue and identify persuasion strategy—but the copy I can read is only the abstract. The body text is encoding-corrupted and carries an arXiv watermark for a different paper (2508.05615v2, cs.CV). So the science is currently unverifiable, not wrong.\n\nWhat's new and reasonable: prior probe work covered sentiment and political perspective; applying the same tool to cognitive-science categories (persuasion success, persuadee personality, strategy) is a legitimate extension. The temporal localization claim is concrete and falsifiable, and the probe-vs-prompting efficiency comparison is worth taking seriously. If it holds up, it's a cheap diagnostic for deception and manipulation research.\n\nSoft spots, in proportion: the biggest is label provenance, stated nowhere in the abstract. Whether the persuasion point is an externally labeled event or a threshold applied to probe outputs completely changes what the result means. There are no probe accuracies, no chance baselines, and no description of the validation protocol in the abstract. The comparison to prompting is asserted, not specified. These are exactly the details referees need. The corrupted text is a practical barrier, not a judgment on the authors; a bad upload or extraction artifact is plausible.\n\nWho this is for: interpretability and AI-safety people working on multi-turn manipulation and deception. They should get a clean PDF and then decide. An editor should ask for a clean version and send it to review if the methods match the abstract. As it stands, I wouldn't cite it, and I wouldn't let the unreadable body be the final impression.","headline":"Interesting, testable idea about localizing persuasion with linear probes, but the body is unreadable and watermarked for a different arXiv ID, so the science is currently unverifiable.","tokens_in":16025,"tokens_out":2346,"would_cite":false,"duration_ms":24797,"reading_group":"maybe","serious_thinker":"unclear","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Linear probes trained on cognitive-science labels can locate the turn at which an LLM persuades its partner, and for strategy detection they match or beat prompt-based analysis at lower cost.","keywords":["linear probes","persuasion dynamics","multi-turn conversations","large language models","internal representations","persuasion strategy","prompting","cognitive science"],"falsifier":"Take a set of multi-turn conversations in which humans independently mark the turn where the persuadee's stated position changes. If a probe trained on the paper's labels cannot rank that human-marked turn above chance among all turns, the localization claim is refuted.","tokens_in":15187,"feed_emoji":"💬","tokens_out":6775,"duration_ms":58495,"temperature":0.7,"pith_summary":"This paper tries to establish that the moment-by-moment mechanics of LLM persuasion are readable from the model's internal representations with a linear probe: a simple classifier trained on hidden states. Drawing labels from cognitive-science studies of persuasion—success, persuadee personality, and strategy—the authors claim the probes capture persuasion at the level of individual conversations and of whole datasets, and can flag the turn at which the persuadee is won over. If true, this gives a cheap, scalable alternative to prompting-based analysis for studying how models persuade, and opens the same technique for other social behaviors such as deception and manipulation.","feed_headline":"Linear probes pinpoint the turn where an LLM persuades","feed_subtitle":"Trained on cognitive-science labels, they match or beat prompt-based analysis when detecting persuasion strategy.","key_machinery":"Linear probes—trained linear classifiers applied to a model's hidden states—are the central instrument. Each probe learns a single direction that predicts one label, such as 'persuaded' or a strategy category, from the model's internal activations at each turn. That one direction does the argument's work: because it can be evaluated at every turn without generating text, it yields a per-turn persuasion score whose trajectory localizes the moment of persuasion, and it scales to entire datasets far more cheaply than asking the LLM to analyze its own output.","core_discovery":"The paper's central claim is that lightweight linear probes trained on LLM hidden representations can track persuasion dynamics in natural multi-turn conversations. Probes trained to predict persuasion success, persuadee personality, and persuasion strategy are said to capture these aspects at the sample level—identifying the particular turn where a persuadee is persuaded—and at the dataset level, revealing where persuasive success generally occurs. The authors further claim that probe-based analysis is faster than prompting-based approaches and performs just as well or better, especially for strategy detection. The upshot is that persuasion is not an opaque emergent behavior but a structure","pith_inferences":["Editorially, the localization claim invites a causal follow-up the paper does not make: if the probe direction is manipulated or erased in the model, persuadee behavior should shift; that would test whether the captured signal drives persuasion rather than merely correlating with it.","Editorially, the match-or-beat-prompting comparison depends on the particular strategies tested; with rarer or more complex strategies prompting may regain the edge, so the relative advantage should not be read as universal.","Editorially, if the localization result survives comparison to human-annotated turning points, probe scores could themselves serve as cheap labels for building larger persuasion datasets."],"forward_implications":["If probes localize persuasion turns, researchers can trace how persuasive pressure builds and breaks within a conversation instead of only measuring final outcomes.","Dataset-level probe patterns would make large-scale analyses of persuasion feasible where prompting every conversation is too expensive.","Strategy detection with probes matching prompting suggests internal-state analysis can substitute for self-report in other social behaviors.","The speed advantage would allow comparing how different models persuade across many conversations and long multi-turn exchanges."],"supporting_citations":[],"fun_headline_variants":["Probes find the exact turn LLMs persuade","Lightweight probes map LLM persuasion turns","Probes reveal when LLM persuasion succeeds","Linear probes beat prompting at persuasion cues","Track LLM persuasion turn-by-turn with probes"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The load-bearing premise is that the labels for persuasion success, persuadee personality, and strategy are valid and come from a source independent of the probe, rather than being defined by the probe's own scores.","fun_headline_variants_meta":{"raw":{"variants":["Probes find the exact turn LLMs persuade","Lightweight probes map LLM persuasion turns","Probes reveal when LLM persuasion succeeds","Linear probes beat prompting at persuasion cues","Track LLM persuasion turn-by-turn with probes"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000135,"raw_usage":{"total_tokens":963,"prompt_tokens":710,"completion_tokens":253,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":454,"completion_tokens_details":{"reasoning_tokens":186}},"tokens_in":454,"tokens_out":253,"duration_ms":3277,"temperature":1.0,"reasoning_tokens":186,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T23:11:52.259734+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a set of multi-turn conversations in which humans independently mark the turn where the persuadee's stated position changes. If a probe trained on the paper's labels cannot rank that human-marked turn above chance among all turns, the localization claim is refuted.","supporting_citations":[],"review_version":1}