{"id":"d9de7e62-83ab-411e-8c2e-39d93a2afdea","arxiv_id":"2606.06674","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Open-ended preference data reveals substantial plurality in what people want from AI and divergent interpretations of shared values such as truthfulness.","lead":"The paper analyzes 1,500 open-ended responses from the PRISM dataset across 75 countries on desired AI behaviors. It finds high diversity in preferences and that common terms like truthfulness carry incompatible meanings, challenging standard RLHF alignment methods.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Qualitative coding of divergent truthfulness meanings lacks reported inter-rater reliability or bias controls","rationale":"The reader's weakest_assumption matches the load-bearing step exactly. Because the paper is a qualitative analysis whose headline policy implication follows from the accuracy of those meaning assignments, the absence of reliability metrics keeps the central claim under-supported rather than refuted. No other technical inconsistency (e.g., sampling frame or external hallucination rates) is more directly tied to the 'single reward model cannot capture' assertion.","tokens_in":1789,"tokens_out":338,"duration_ms":14890,"concrete_test":"Release the coded subset of truthfulness responses with original text; have two independent annotators (blind to paper categories) re-code a random 200-response sample into the three meaning buckets and compute Cohen's kappa. If kappa < 0.65 or category distributions shift >20%, the incompatibility claim is sensitive to interpretive choices.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The claim that 49% requesting truthfulness but with incompatible epistemological bases (sourced claims vs. expert opinion vs. unpopular views) cannot be captured by a single reward model depends on the authors' interpretation of open-ended responses as revealing true incompatibility rather than surface variation. The paper provides no quantitative details on coding protocol, number of coders, inter-rater agreement, or steps taken to mitigate researcher framing when labeling responses as having 'distinct, potentially incompatible' bases. Selection effects in the 1,500 PRISM responses and the leap from observed lexical divergence to 'epistemic violence' in alignment also rest on this unvalidated interpretive layer.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper analyzes 1,500 open-ended responses from the PRISM dataset across 75 countries to map user preferences for AI systems. It reports that preferences are highly diverse, with most values requested by fewer than 25% of respondents and truthfulness the only exception at 49%; however, open-ended elaborations on truthfulness reveal divergent epistemological bases (sourced claims vs. expert opinion vs. unpopular views). The work further identifies controversial features (e.g., human-likeness, guardrails) and contextual distinctions (default vs. requested behavior) that binary preference comparisons cannot capture, concluding that these pluralities cannot be aggregated into single reward models and that current RLHF practices therefore fail to identify actual preferences.","tokens_in":1932,"tokens_out":571,"duration_ms":25374,"significance":"If the qualitative interpretations are robust, the findings provide concrete empirical grounding for known limitations of RLHF, showing that lexical agreement on values like truthfulness masks incompatible underlying demands. This could motivate development of alignment methods that handle preference plurality and context rather than forcing aggregation, and the use of a large multi-country open-ended dataset is a strength relative to typical binary-feedback studies.","major_comments":[{"comment":"The central claim that 49% of respondents request truthfulness but with incompatible epistemological bases (and thus cannot be captured by a single reward model) rests entirely on the authors' qualitative coding of open-ended responses. No details are supplied on the coding protocol, number of coders, inter-rater reliability (e.g., Cohen's kappa), coder training, or steps to mitigate researcher framing bias. This absence is load-bearing for the incompatibility interpretation and for the broader argument about epistemic violence in alignment.","section":"Methods / Results (qualitative analysis of truthfulness responses)"},{"comment":"The manuscript states concrete demographic and exclusion criteria are absent from the abstract and provides no information on sample demographics, response exclusion rules, or representativeness of the 1,500 PRISM responses. These omissions undermine the generalizability claim that current methods 'fail to identify actual preferences' across populations.","section":"Section describing the PRISM dataset and sample"}],"minor_comments":[{"comment":"The abstract is dense and could be split for clarity; the phrase 'epistemic violence' is used without a direct citation to the source work that introduced the characterization.","section":"Abstract"},{"comment":"Figure or table summarizing the distribution of requested values (beyond the 49% truthfulness figure) would help readers assess the 'most values requested by fewer than a quarter' claim.","section":"Results"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, indicating revisions where appropriate.","responses":[{"response":"We agree that the manuscript lacks sufficient detail on the qualitative coding process, which is important for evaluating the robustness of the incompatibility claims. The coding was conducted iteratively by the lead authors through repeated review and discussion to identify and categorize the distinct epistemological bases (e.g., sourced claims, expert opinion, unpopular views). No formal inter-rater reliability statistic such as Cohen's kappa was computed, and coder training was informal. In revision, we will add a dedicated Methods subsection describing the full protocol, the number of coders, the consensus process, and any steps taken to reduce framing bias (such as using multiple independent readings before joint discussion). This addition will directly support the interpretation without altering the core findings.","revision_made":"yes","referee_comment":"[Methods / Results (qualitative analysis of truthfulness responses)] The central claim that 49% of respondents request truthfulness but with incompatible epistemological bases (and thus cannot be captured by a single reward model) rests entirely on the authors' qualitative coding of open-ended responses. No details are supplied on the coding protocol, number of coders, inter-rater reliability (e.g., Cohen's kappa), coder training, or steps to mitigate researcher framing bias. This absence is load-bearing for the incompatibility interpretation and for the broader argument about epistemic violence in alignment."},{"response":"We acknowledge that the manuscript does not include a self-contained summary of the PRISM sample characteristics, exclusion rules, or representativeness, which limits assessment of generalizability. Although the PRISM dataset paper provides these details, the current work does not extract or discuss them. We will revise the manuscript to add a subsection on the dataset and sample, including available demographic breakdowns, any exclusion criteria applied to arrive at the 1,500 responses, and an explicit discussion of representativeness limitations. This will better ground the claims about preferences across populations.","revision_made":"yes","referee_comment":"[Section describing the PRISM dataset and sample] The manuscript states concrete demographic and exclusion criteria are absent from the abstract and provides no information on sample demographics, response exclusion rules, or representativeness of the 1,500 PRISM responses. These omissions undermine the generalizability claim that current methods 'fail to identify actual preferences' across populations."}],"tokens_in":1533,"tokens_out":521,"duration_ms":37719,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"Colleague,\n\nThe core observation is that open-ended answers from 1500 PRISM responses across 75 countries show most requested values come from under a quarter of people, with truthfulness at 49% but carrying incompatible interpretations like sourced claims versus expert opinion versus unpopular views. This is presented as evidence that single reward models cannot capture the spread.\n\nThe shift to open-ended data is the clearest difference from standard preference work. It surfaces that some capabilities are outright divisive and that users draw contextual lines binary comparisons would miss. That part is useful for anyone thinking about why alignment techniques still produce outputs that contradict stated user priorities.\n\nThe weak point is the interpretive layer. The claims about divergent epistemological bases depend on how the responses were coded and labeled as incompatible. The abstract supplies no numbers on coders, agreement rates, or steps to check for framing effects, and the stress-test note flags the same gap. Without those details it is difficult to separate real divergence from researcher reading. The sample diversity is a plus, but the lack of reported demographics or exclusion rules leaves the same uncertainty.\n\nThis is for alignment researchers who already suspect current aggregation methods lose signal. It does not deliver a new method or dataset that would change practice on its own.\n\nIt should go to peer review. The question is worth asking, but the methods section needs to be explicit enough for others to evaluate the coding and the leap from lexical variation to structural limits on reward models.","headline":"The paper shows plurality in what people want from AI via open-ended PRISM responses, but the qualitative claims on meaning divergence rest on unvalidated coding.","tokens_in":2369,"tokens_out":372,"would_cite":false,"duration_ms":18439,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"People's preferences for AI diverge sharply, with even 'truthfulness' carrying incompatible meanings across respondents that single reward models cannot capture.","keywords":["AI alignment","RLHF","preference plurality","truthfulness","human feedback","preference elicitation","epistemic violence"],"falsifier":"A single reward model trained only on binary comparisons that produces outputs matching the full range of definitions for truthfulness given in the open responses.","tokens_in":2710,"feed_emoji":"","tokens_out":590,"duration_ms":20446,"temperature":0.7,"pith_summary":"The paper analyzes 1,500 open-ended responses from the PRISM dataset across 75 countries to identify what users actually want from AI systems. It shows that most requested values come from fewer than a quarter of people, except for truthfulness at 49 percent. The same terms reveal divergent definitions, such as truthfulness meaning sourced claims for some and expert opinions or unpopular views for others. Capabilities like human-like behavior and features like guardrails prove controversial, with some wanting them and others rejecting them. People also draw contextual lines, such as default versus requested behaviors, that binary comparison methods miss.","feed_headline":"Diverse responses show truthfulness means incompatible things to users","feed_subtitle":"Analysis of 1,500 open answers finds most AI values wanted by under 25 percent and shared terms hiding divergent definitions.","key_machinery":"Qualitative coding of open-ended responses from the PRISM dataset that surfaces preference plurality and incompatible epistemological bases behind shared terms.","core_discovery":"The central claim is that preference plurality and semantic divergence in open-ended responses expose fundamental limits in RLHF alignment: when nearly half request truthfulness but define it differently, and when contextual distinctions exceed binary comparisons, current aggregation into a single reward model flattens situated signals and fails to match actual user demands, as seen in persistent high hallucination rates despite clear accuracy preferences.","pith_inferences":["Alignment systems may require multiple or context-switching models rather than one universal preference function.","Future preference datasets would benefit from including open-ended questions alongside ratings to avoid flattening signals.","The findings connect to broader questions about how to handle value pluralism when building public AI systems."],"forward_implications":["A single reward model is unlikely to satisfy the varied definitions of truthfulness.","Binary comparisons cannot encode distinctions between default and requested behaviors.","Controversial features like guardrails will produce both demand and rejection.","Persistent hallucinations indicate that current methods do not identify the accuracy preferences expressed in the data."],"fun_headline_variants":["Truthfulness defined in multiple incompatible ways","Most desired AI traits from under 25 percent of users","Semantic differences in preferences defy aggregation","User context distinctions exceed binary comparisons"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The qualitative reading of divergent meanings in the responses correctly identifies incompatible bases without substantial researcher bias or sampling effects.","fun_headline_variants_meta":{"raw":{"variants":["Truthfulness defined in multiple incompatible ways","Most desired AI traits from under 25 percent of users","Semantic differences in preferences defy aggregation","User context distinctions exceed binary comparisons"]},"model":"grok-4.3","cost_usd":0.005541,"raw_usage":{"total_tokens":2701,"prompt_tokens":754,"num_sources_used":0,"completion_tokens":51,"cost_in_usd_ticks":55412000,"prompt_tokens_details":{"text_tokens":754,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1896,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":754,"tokens_out":51,"duration_ms":21217,"temperature":1.0,"reasoning_tokens":1896,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T01:29:40.037063+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A single reward model trained only on binary comparisons that produces outputs matching the full range of definitions for truthfulness given in the open responses.","supporting_citations":[],"review_version":1}