{"id":"73b9df23-f1d3-4b44-8b4b-c0fd9de83fb3","arxiv_id":"2608.00339","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"In matched data-analysis tasks, AI agents draw prior-aligned conclusions from identical numerical evidence, with process traces suggesting motivated reasoning in some cases.","lead":"AI agents given identical numbers but different real-world labels—vaccine vs. alcohol, U.S. vs. Venezuela, China vs. Taiwan—reach different conclusions that line up with the agents' prior beliefs. The study maps a concrete risk in letting AI run consequential data analyses: unstated priors can steer the result.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 'prior-aligned' claim is under-identified: measured priors are collinear with frame-label semantics in 11 of 12 agent–domain cells, and the one orthogonal cell (GPT-5.6 Sol, geopolitics) shows no framing effect; Fig. 4's belief–behavior slope may re-measure label content rather than an independe","rationale":"I read the paper as a three-level argument: framing effects (L1) are robust and well controlled; the directional, prior-aligned attribution (L2) is the paper's central interpretation; motivated reasoning (L3) is explicitly presented as suggestive. The delegation-risk headline depends on L2. The design has real strengths — numerically identical data across frames, ten dataset versions, five runs per cell, a null-effect reversal that answers the ground-truth confound, a scope-condition manipulation, and recorded tool traces. My concern targets the identification of L2. The experimenter selected frames whose real-world plausibility ordering matches the elicited ordering almost perfectly ('these results largely confirm that the chosen frames successfully induce a strong separation,' §4), so the prior measure is collinear with the frame label content. The belief–behavior correlation in Fig. 4 is therefore consistent with a single latent cause (lexical/semantic associations in the training data) producing both measures; the elicitation does not demonstrate an independent prior. The one cell that could break the collinearity — GPT-5.6 Sol in geopolitics, where the measured ordering puts State A–State B below China–Taiwan — shows no framing sensitivity in binary conclusions, so the discriminating test is simply not run. I do not regard this as a design failure in bad faith; it is an evidentiary gap the preprint could close, and the paper's own level-ordering anticipates the required evidentiary ladder. The reader's weakest assumption (elicitation stability) is the mechanism through which my concern would either fail or hold; I agree it is the right spot, but the sharper issue is discriminant validity, not merely test–retest reliability. If the proposed test finds that conclusions track per-agent measured priors, L2 is rescued and the conditional verdict stands; if it finds the gradient follows default labels, the paper's contribution reduces to L1, which is a real but less specific finding. Either way the verdict stays CONDITIONAL, so I leave it unchanged while specifying the condition precisely.","tokens_in":16029,"tokens_out":16404,"duration_ms":153662,"concrete_test":"Exploit the measured prior variation orthogonal to label semantics. (1) Split-half the 162 pairwise comparisons per agent–domain (by question stem); if the Bradley–Terry aligned–opposed ordering is not reproducible across halves, frame labels are arbitrary. (2) For GPT-5.6 Sol in geopolitics (priors rank State A–State B least likely), rerun the geopolitical experiment (10 versions × 5 runs) with frames relabeled by GPT's own ordering: prior-aligned = Turkey–Cyprus, prior-opposed = State A–State B. The prior account predicts the lowest affirmative rate and smallest probability estimate in the State A–B frame, and an aligned-minus-opposed contrast at least as large as Turkey–Cyprus-minus-China–Taiwan. If the gradient instead tracks default labels (Turkey > China > State A–B) or is flat, Fig. 4's slope re-measures label content rather than an independent prior.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The strongest claim — conclusions depend on priors 'neither specified in the task nor visible in the decision record' (Abstract) — requires the elicited prior to carry behavioral information beyond the frame labels themselves. It does not: in 11 of 12 agent–domain cells the Bradley–Terry ordering (Appendix A; Fig. 2) re-states the real-world semantics of frames chosen a priori in Section 3 (vaccine=prior-opposed, alcohol=prior-aligned; U.S.=opposed, Venezuela=aligned; China–Taiwan=opposed, Turkey–Cyprus=aligned). Prior and label are collinear, so the positive slope in Fig. 4 is equally consistent with agents pattern-matching on lexical content ('alcohol' vs 'vaccine') as with an independently measured latent belief. The one cell where the two accounts diverge — GPT-5.6 Sol in geopolitics, whose elicited priors rank State A–State B least likely (§4, Fig. 2) — is also where its conclusions are 'largely fixed across frames' (§4, Fig. 3), so it discriminates nothing. The null-effect design (App. C) and cross-design reversal (Table 2) do answer the ground-truth and stable-causal-model alternatives, but both still vary the same semantic labels; the scope-condition experiment (Fig. 6) shows discretion matters, not that priors drive it. Level-1 framing effects are robust; the level-2 'prior-aligned' attribution and the headline delegation risk are under-identified. Table 2's reversal also lacks a formal design×frame interaction test, leaving level-3 evidence 'suggestive,' as the paper itself concedes.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper reports three linked experiments in medicine, election forensics, and geopolitical forecasting in which four AI agents analyze numerically identical datasets under different substantive frames (prior-aligned, neutral, prior-opposed). Prior beliefs are elicited separately via 162 pairwise comparisons per agent–domain and converted to Bradley–Terry log-strengths. The main empirical claims are: (1) framing alone shifts agents' binary conclusions and point estimates; (2) these shifts are prior-aligned, in that the size of the framing effect correlates with the elicited prior gap; and (3) in the medical domain, analytical traces show asymmetric search and specification choice that reverse when the data-generating process is reversed, which the paper interprets as suggestive evidence of motivated reasoning. A scope-condition variant shows that clarifying the target estimand and the post-exposure nature of mediators reduces, but does not eliminate, the framing gap.","tokens_in":16477,"tokens_out":4304,"duration_ms":46199,"significance":"If the findings hold, the paper would make a useful contribution to the emerging literature on delegation of consequential analysis to AI agents. The core Level-1 finding — that identical numerical data produce different conclusions under different substantive labels — is convincingly designed: matched synthetic data, neutral prompts, multiple dataset versions, five runs per cell, and a null-effect reversal. The distinction among framing effects, prior-aligned Bayesian reasoning, and motivated reasoning is conceptually clear and helps structure future work. The scope-condition experiment is a genuine attempt to probe mechanism. However, the paper's headline 'prior-aligned' attribution and the associated delegation risk are not yet identified: the prior measure and the frame labels are collinear in 11 of 12 agent–domain cells, no formal significance tests are reported for the main correlation or the Table 2 reversal, and the one near-orthogonal cell is also one with no framing effect. The manuscript's own hedged language ('suggestive evidence' for motivated reasoning) is appropriate, but the abstract's stronger claim needs additional support. No code or data are currently shipped, l","major_comments":[{"comment":"The central 'prior-aligned' claim is under-identified. The frames are selected in Section 3 because they are believed ex ante to be prior-aligned or prior-opposed (vaccine vs. alcohol, U.S. vs. Venezuela, China–Taiwan vs. Turkey–Cyprus), and the elicited Bradley–Terry ordering in Fig. 2 reproduces exactly that semantic ordering in 11 of 12 agent–domain cells. The positive slope in Fig. 4 therefore cannot distinguish 'the agent's latent prior drives the conclusion' from 'the agent pattern-matches on the lexical/semantic content of the frame label.' The one cell where the measured ordering deviates from the labels' intended semantics (GPT-5.6 Sol in geopolitics, where State A–State B is least likely) is also a cell in which binary conclusions are largely fixed across frames, so it arbitrates nothing. A concrete test would be to include frames whose measured prior ordering is opposite to th","section":"§3, §4 (Figs. 2 and 4)"},{"comment":"The paper asserts 'strongly influenced' and 'significantly lower' without reporting formal tests. Fig. 4 plots 12 observations with no standard error or regression line; Fig. 3 reports overlapping confidence intervals but no model-based comparison; and Table 2's central reversal is described by comparing columns across designs without a design × frame interaction test. Because the Level-3 motivated-reasoning claim rests entirely on the cross-design reversal, the authors should report a formal test (e.g., a mixed model or cluster-robust regression with dataset-version clustering, agent random effects, and a design × frame interaction), as well as a test of the Fig. 4 slope. Without this, the evidence should be described as suggestive only, as the paper itself does in places.","section":"§4, Fig. 3; §4, Fig. 4; Table 2"},{"comment":"The reliability of the prior measure is not established. The 162 pairwise answers are pooled across nine stems and three phrasings, but the paper reports no test-retest stability, split-half reliability, or consistency across stems. If the elicited ordering is an artifact of prompt wording or presentation order rather than a stable latent belief, the correlation in Fig. 4 would be preselected by construction. At minimum, the paper should report per-stem agreement, split-half Bradley–Terry estimates, and a stability check across presentation orders.","section":"§3, 'Prior belief elicitation'"}],"minor_comments":[{"comment":"No code, prompts beyond the printed ones, or anonymized data are shipped, despite the heavy reliance on synthetic data and scripted agent runs. Providing these would substantially aid verification and re-analysis.","section":"General"},{"comment":"The handling of confidence intervals is inconsistent: Appendix C states binary-outcome intervals are clustered by dataset version, but the main text Fig. 3 does not specify whether the displayed intervals are clustered or adjusted for the five runs per version–frame cell. Please harmonize the description.","section":"§4, Fig. 3 legend and Appendix C Fig. C1"},{"comment":"Several references are dated 2026, which is plausible for the arXiv date but may be difficult to verify; please ensure citation details are complete and available.","section":"References"},{"comment":"The sentence 'The average estimated odds ratio is significantly lower when the exposure is described as a vaccine rather than alcohol' uses 'significantly' without a test statistic or p-value. Please either report the quantitative test or rephrase to avoid the statistical claim.","section":"§4, 'Analytical conclusions'"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: the paper has a genuinely clean demonstration that AI agents reach different conclusions from identical data when the substantive labels change. The three-level framework is useful, and the null-effect reversal plus scope-condition experiments are thoughtful. But the headline attribution — that the framing effect is driven by agents' prior beliefs — is not identified by this design. The prior-elicitation stage asks the agent to rank the very propositions that define the frames. For 11 of 12 agent-domain cells, the elicited ordering simply restates the semantic ordering you'd get from the labels: alcohol is a more likely cause of cancer than vaccine, Venezuela is more likely to be manipulated than the U.S., Turkey–Cyprus is more likely than China–Taiwan. So the positive slope in Fig. 4 is equally compatible with the agent pattern-matching on the label 'alcohol' vs 'vaccine' in the analysis task as with an independently measured prior driving the outcome. The one cell that could discriminate — GPT-5.6 Sol in geopolitics, where the prior ordering is atypical — shows no framing effect, so it provides no leverage. To make the prior-aligned claim you'd need a design where the prior measure varies independently of the frame label, e.g., the same label with a manipulated or counterbalanced prior, or a wider set of scenarios with within-label variation.\n\nWhat the paper does well: matched numerical data, neutral prompts, multiple runs per cell, a real null-effect reversal in medicine, and a scope-condition manipulation that shows discretion matters. The level-1 finding — framing changes conclusions, search effort, and specification choice — is robust and worth reporting. The motivated-reasoning evidence in Table 2 is suggestive but under-analyzed: no formal test of the cross-design reversal, no significance tests on the main correlation, and no data/code shipped.\n\nMy recommendation: send it to peer review, but ask the authors to strengthen the identification of the prior-aligned claim and to add formal tests. As it stands, the paper overstates what the design can show. A reviewer should push on the collinearity issue before publication.","headline":"Framing effects are real, but the paper's prior-aligned attribution is under-identified because the prior measure is collinear with the frame labels.","tokens_in":16919,"tokens_out":4079,"would_cite":false,"duration_ms":39825,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"AI agents draw different conclusions from identical numerical data when the substantive framing changes, and the difference tracks their prior beliefs.","keywords":["AI agents","framing effects","prior beliefs","motivated reasoning","Bayesian updating","election forensics","medical data analysis","delegated decision-making"],"falsifier":"Run the experiment again with the prior-aligned and prior-opposed frames swapped relative to the elicited ordering—that is, label the proposition the agent regards as least likely as 'aligned'—and check whether the conclusion gap reverses. If it does not, the correlation between prior beliefs and conclusions would be exposed as an artifact of the elicitation's framing rather than a stable belief driving behavior.","tokens_in":15931,"feed_emoji":"🤖","tokens_out":4301,"duration_ms":43577,"temperature":0.7,"pith_summary":"This paper tries to establish that AI agents, left to their own analytical discretion, draw different conclusions from identical numerical data when only the substantive framing changes, and that the direction of the difference tracks the agent's prior beliefs. It demonstrates this in three high-stakes domains—medicine, election forensics, and geopolitical forecasting—using synthetic datasets with forking analytical paths so that defensible specifications can lead to different answers. The paper further argues that some of the behavior resembles motivated reasoning, not just Bayesian updating, because the same analytical choice is used selectively depending on whether it moves the estimate toward the prior-supported conclusion. The stakes: when users delegate data analysis to agents, the final decision may depend on beliefs the user never supplied and cannot see in the decision record.","feed_headline":"AI agents read identical data differently when the label changes","feed_subtitle":"In medicine, elections, and geopolitics, the same numbers yield different answers—tracking what the agent already believes.","key_machinery":"The matched-data framing design is the central mechanism. First, each agent's relative prior beliefs are elicited from 162 pairwise comparisons per domain, scored with a paired-comparison log-strength model. Then the same synthetic dataset is presented under a prior-aligned, neutral, and prior-opposed frame, with only substantive labels changed. The analytical traces—tool calls, model specifications, and which computed estimate is finally reported—let the paper separate a general framing effect from prior-aligned (Bayesian) reasoning and from motivated reasoning, using the cross-design reversal of specification choice as the key diagnostic.","core_discovery":"The paper's central claim is that AI agents show prior-aligned framing effects in open-ended data analysis: when the same numerical dataset is presented under different substantive labels, agents' binary conclusions and point estimates shift in the direction of their independently measured prior beliefs. In the medical, election, and geopolitical tasks, affirmative conclusions are most frequent in the prior-aligned frame and least frequent in the prior-opposed frame, with the neutral frame in between, and the size of the conclusion gap tracks the gap in elicited prior strength. The paper also claims suggestive evidence of motivated reasoning: agents search longer and fit post-exposure specif","pith_inferences":["Beyond the paper: the same design could be run with priors manipulated directly via system prompts or context passages rather than inferred from labels; if the conclusion gap follows the manipulated prior, it would confirm that the effect is driven by latent belief rather than surface wording.","Beyond the paper: if hidden priors bias conclusions in the direction of the agent's baseline beliefs, then ensembles of agents with diverse priors, or aggregating answers across frames, could serve as a debiasing strategy—a testable extension the paper does not pursue.","Beyond the paper: the scope-condition results suggest a practical oversight rule—requiring agents to report the specification search path and alternative specifications could expose prior-driven selectivity even when the final answer looks plausible."],"forward_implications":["If the claim holds, a user who delegates an analysis to an AI agent cannot assume the conclusion reflects only the supplied evidence; it may also reflect the agent's prior beliefs, making the decision record incomplete.","Prior-aligned behavior is not inherently a defect: an agent that updates accurately from a reasonable prior may reach a better answer; the risk is that the user neither specified nor knows the prior.","Reducing analytical discretion—by naming the target estimand and clarifying the timing of variables—shrinks framing effects, but the residual gaps mean explicit guidance does not fully neutralize prior influence.","The cross-design reversal of specification choice provides an observable signature that distinguishes motivated search from straightforward Bayesian updating in agentic analyses."],"fun_headline_variants":["Identical data, different AI answers when framing shifts","AI agents let prior beliefs skew their data conclusions","Framing changes how AI agents analyze the same numbers","AI shows motivated reasoning in high-stakes data tasks","Prior beliefs alter AI conclusions even with fixed evidence"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the 162 pairwise comparisons elicit a stable prior that actually drives the later analysis, rather than being an artifact of prompt wording, presentation order, or the specific way the prior question is asked.","fun_headline_variants_meta":{"raw":{"variants":["Identical data, different AI answers when framing shifts","AI agents let prior beliefs skew their data conclusions","Framing changes how AI agents analyze the same numbers","AI shows motivated reasoning in high-stakes data tasks","Prior beliefs alter AI conclusions even with fixed evidence"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000191,"raw_usage":{"total_tokens":1140,"prompt_tokens":663,"completion_tokens":477,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":407,"completion_tokens_details":{"reasoning_tokens":402}},"tokens_in":407,"tokens_out":477,"duration_ms":5333,"temperature":1.0,"reasoning_tokens":402,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T00:40:13.604974+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the experiment again with the prior-aligned and prior-opposed frames swapped relative to the elicited ordering—that is, label the proposition the agent regards as least likely as 'aligned'—and check whether the conclusion gap reverses. If it does not, the correlation between prior beliefs and conclusions would be exposed as an artifact of the elicitation's framing rather than a stable belief driving behavior.","supporting_citations":[],"review_version":1}