{"id":"c11a63f6-ebab-4888-9242-77295f13d12f","arxiv_id":"2509.08494","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A new benchmark finds low to moderate human agency support in 20 LLM assistants across six dimensions.","lead":"HumanAgencyBench uses LLMs to generate thousands of test user queries and score assistant responses on six dimensions of human agency support. Across 20 frontier LLMs, scores are low to moderate, with Anthropic's Claude models leading overall but scoring lowest on avoiding value manipulation.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Construct validity is unestablished: human-LLM agreement only validates rubric application, not that HAB scores track real human agency; a criterion-validity test is needed.","rationale":"The reader's weakest assumption—that the six rubric-defined behaviors are valid operationalizations of human agency support—is the heart of the matter. My stress test sharpens this into a specific methodological gap: the human evaluation is circular because annotators applied the same rubric rather than independently judging agency, so it cannot supply criterion validity. The paper is transparent about this in Section 5 and provides real supporting evidence: reproducible code/data, sensitivity analyses across rubric wordings and orderings, LLM-evaluator agreement, and a preregistered human study. That evidence supports the benchmark as a reliable measure of the rubric-defined behaviors, but not as a measure of human agency per se. This is a genuine soft spot, but it does not move the reader's conditional verdict: the paper frames itself as a proof-of-concept and explicitly calls for empirical development of the construct. I would keep the verdict CONDITIONAL, with the condition being demonstration of criterion validity or a clearly scoped reinterpretation of HAB as measuring a specific normative theory of agency-supportive behavior rather than agency itself.","tokens_in":28165,"tokens_out":3900,"duration_ms":47406,"concrete_test":"Run a preregistered study in which participants complete realistic tasks with assistants that score high vs low on HAB (e.g., Claude-3.5-Sonnet-20241022 vs GPT-4.1 on Ask Clarifying Questions), then measure perceived and behavioral agency with an established instrument (e.g., Sense of Agency Scale, Tapal et al. 2017) and decision/learning outcomes. If HAB score differences do not predict these external agency measures, the central claim fails criterion validity.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that HAB measures human agency support requires criterion validity: HAB scores should correlate with externally measured agency outcomes. The paper's human study (Sec. 4.2) is not a construct validation—it asked 468 Prolific workers to annotate responses using the same rubric issues ('make the study context as similar as possible to the evaluation materials input into the evaluator LLMs'), so agreement with o3 (α=0.583) only shows the LLM judge can reproduce human applications of the rubric. The rubric itself—six behaviors, their direction (e.g., deferring decisions is always supportive), and deduction weights—is asserted from agency theory and explicitly flagged in Section 5 as embedding assumptions 'that should each be the subject of thorough conceptual and empirical development.' Without external validation against user-perceived or behavioral agency, the headline findings (e.g., Anthropic most supportive overall, low-to-moderate support) could be artifacts of rubric choices rather than properties of the assistants. This is the single load-bearing gap: if the rubrics mis-specify what supports agency, every HAB score and cross-developer comparison in Figure 4/Table A1 is uninterpretable as a measure of human agency.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"HumanAgencyBench (HAB) is presented as a scalable, adaptive benchmark for measuring whether LLM-based assistants support human agency along six dimensions: asking clarifying questions, avoiding value manipulation, correcting misinformation, deferring important decisions, encouraging learning, and maintaining social boundaries. The benchmark is constructed by using GPT-4.1 to simulate 3,000 user-query candidates per dimension, validating and diversity-sampling them down to 500 per dimension, and using o3 as the primary judge with a deduction-based rubric. The authors evaluate 20 contemporary LLMs, report low-to-moderate agency support overall, and find substantial variation across developers and dimensions—e.g., Anthropic models score highest overall but lowest on Avoid Value Manipulation. The paper includes sensitivity analyses over rubric wording/orderings, agreement among four LLM judges, a preregistered human study with 468 annotators on 900 responses, and open-source code/data. The authors explicitly acknowledge in Section 5 that the six dimensions embed contested assumptions about what behaviors are agency-supporting, and that many agency effects are subtle and long-term.","tokens_in":28487,"tokens_out":3185,"duration_ms":43482,"significance":"If the benchmark is accepted as a valid measure of human agency support, it would be a useful complement to existing alignment and safety evaluations, opening a new axis of assessment that is currently absent from standard benchmark suites. The pipeline is transparent and reproducible, with machine-readable rubrics, a public codebase, and preregistered human validation—these are real strengths. The results are also falsifiable in the sense that they are based on a concrete operationalization with explicit scoring rules, and the sensitivity analyses (Krippendorff's alpha ≈ 0.90 for wording/order permutations) show that the scoring procedure itself is stable under cosmetic rubric changes. However, the paper's central claim—that HAB measures human agency support—requires the six rubric-defined behaviors to be valid operationalizations of agency, and that link is currently asserted from theory and explicitly flagged by the authors as needing further development. The contribution is therefore better characterized as a well-built measurement instrument requiring stronger construct validation before its headline comparisons are interpreted as statements about human agency.","major_comments":[{"comment":"The construct-validity gap is load-bearing. The human study asked 468 Prolific workers to annotate responses using the same evaluation rubric as the LLM judge; the resulting agreement (α = 0.583) shows that o3 can approximate human applications of the rubric, but it does not establish that HAB scores correspond to actual human agency outcomes, user-perceived autonomy, or behavioral measures. The authors concede in Section 5 that 'these six dimensions of agency embed various assumptions about agency theory ... that should each be the subject of thorough conceptual and empirical development.' Without a criterion-validity study (e.g., correlating HAB scores with measures of user autonomy, decision satisfaction, or longitudinal agency-relevant behavior, or at least with independent expert judgments made without the rubric), the empirical claims in Figure 4 and Table A1 (e.g., 'Anthropic most","section":"Section 4.2 and Section 5"},{"comment":"The numerical scores rely on arbitrary deduction weights that are not justified or subjected to sensitivity analysis. For example, Maintain Social Boundaries assigns deductions of -7, -5, -4, -2, -2, and -2 to its six criteria, while Ask Clarifying Questions uses -10, -4, -3, -2. These choices determine the 0–10 scale, yet the reported statistical analyses (means, standard errors, paired t-tests with p<0.01) treat this scale as interval-level and use it to make cross-developer comparisons. The sensitivity checks in Section 4 vary rubric preamble and ordering, not the deduction magnitudes; it is plausible that a different but equally reasonable weighting scheme would change which models are deemed most supportive. The authors should either provide a principled justification for the weights, report robustness of the rankings and averages under reasonable weight perturbations, or explicitly","section":"Appendix D and Section 4"},{"comment":"Dimension-level human–LLM agreement is too low in some cases to support the granular claims made from those scores. Encouraging Learning, which is a major dimension in the overall average, has o3–human agreement α = 0.290 with a 95% CI [0.153, 0.422]—near-zero reliability—and the paper itself notes that manual inspection suggested genuine ambiguity in what counts as 'providing ways to continue learning.' Nonetheless, Encourage Learning scores are used without caveat in the overall HAB index and in comparisons such as xAI having the highest Encourage Learning score via Grok-3 (Table A1). The authors should either exclude or down-weight dimensions below a reliability threshold, report confidence intervals that include judge-related uncertainty, or clearly flag such dimension-specific results as exploratory.","section":"Section 4.2 and Table A1"}],"minor_comments":[{"comment":"Headings render inconsistently as 'A void Value Manipulation' and 'D erefer Important Decisions'; likely a formatting artifact, but should be corrected.","section":"Throughout"},{"comment":"The validation step retains 'the 2000 tests assigned the highest validation scores' before clustering to 500; the justification for retaining 2,000 and the validation-rubric criteria are not given beyond the prompt excerpts in Appendix A. A short explanatory paragraph on why 2,000 is the right retention point would improve reproducibility.","section":"Section 3.1"},{"comment":"The preregistration link is included in a footnote; consider also stating the preregistered primary hypotheses in the main text so readers can assess confirmatory versus exploratory claims.","section":"Section 4.2"},{"comment":"The rubrics list deduction criteria with no specification of how borderline behaviors are adjudicated (e.g., the distinction between 'does not explicitly correct' and 'does not provide evidence' in Correct Misinformation). Providing a short annotation guideline for each dimension would reduce ambiguity.","section":"Appendix D"},{"comment":"The figure is helpful but mixes user-query text, model responses, and evaluator output without clear labels for which parts are inputs versus outputs. A simplified version or explicit callouts would improve readability.","section":"Figure 2"},{"comment":"The abstract says 'low-to-moderate agency support' but the thresholds for low/moderate are not defined anywhere. State what score ranges correspond to low, moderate, and high support, or avoid the qualitative labels.","section":"Abstract and Figure 1"}],"recommendation":"major_revision","confidential_remarks":"The authors have built a careful, transparent evaluation pipeline and are appropriately cautious in their limitations section. My main concern is that the paper's title and abstract make strong claims about measuring human agency support, while the evidence supports only that the instrument reliably applies the authors' rubric. The missing criterion-validity study is a substantial, addressable gap; if the authors can add external validation or substantially qualify the framing, the paper would be a useful contribution to AI evaluation. I also note that the paper cites several works by one of its own authors heavily; this is not inherently problematic, but the novelty of the 'six dimensions' relative to prior agency frameworks could be stated more precisely."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"What you should know: this is a serious, clearly written proposal for turning 'human agency support' into a measurable benchmark, and it ships real assets—an open pipeline, 500 LLM-simulated tests per dimension across six dimensions, sensitivity analyses, a preregistered human study, and results for 20 models. That is more than most sociotechnical evaluation papers deliver.\n\nThe genuinely new part is the packaging: six rubric-defined behaviors (asking clarifying questions, avoiding value manipulation, correcting misinformation, deferring important decisions, encouraging learning, maintaining social boundaries) integrated into one adaptive LLM-as-a-judge benchmark. Individual dimensions overlap with prior work on sycophancy, clarification, and misinformation, but the integrated framework is a real contribution. The authors are also admirably honest: Section 5 explicitly says the six dimensions embed assumptions about agency that need conceptual and empirical development, and they report moderate human-LLM agreement rather than hiding it.\n\nThe load-bearing soft spot is construct validity, and the stress-test note is right about that. The human study (Section 4.2) asked annotators to apply the same rubric issues used by the LLM evaluator, so the α=0.583 agreement validates the judge's ability to follow the rubric, not that the rubric tracks human agency in the world. The direction of scoring—e.g., always deferring important decisions is agency-supporting, always declining social relationships is agency-supporting—is asserted from agency theory, and the abstract's claim that HAB 'measures' human agency support goes beyond what is actually demonstrated. The cross-developer comparisons (Anthropic highest overall but lowest in Avoid Value Manipulation) could be artifacts of rubric choices. That said, this is a proof-of-concept weakness, not a fatal one: the paper is candid about it, and the fix is fairly clear—criterion-validity testing against user-perceived agency or behavioral outcomes.\n\nMinor concerns: human-LLM agreement is moderate overall and quite low for Encourage Learning (α=0.290); the test set is generated by GPT-4.1 under hand-written rubrics, so the domain coverage is narrower than the agency construct; and the deduction weights are arbitrary. None of these are deal-breakers at this stage.\n\nWho this is for: researchers working on AI evaluation methodology, alignment targets, and sociotechnical measurement. It deserves a serious referee—an editor should send this to review, not desk-reject it. The main revision request should be to either add external validation or reframe the paper's claims as 'a proposed operationalization' rather than 'a measure of human agency support.' I would cite it in my own work and probably bring it to reading group, with the validity caveat stated upfront.","headline":"A transparent, well-built first attempt at measuring human agency support in LLMs, held back by the expected construct-validity gap: the rubric is plausible but unvalidated, and the human study only shows the LLM judge can apply that rubric like humans.","tokens_in":28902,"tokens_out":1586,"would_cite":true,"duration_ms":18326,"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":"A new benchmark measures whether AI assistants support human agency and finds contemporary chatbots do so only weakly.","keywords":["human agency","AI assistants","benchmark","LLM-as-a-judge","AI alignment","clarifying questions","misinformation","social boundaries"],"falsifier":"Give users controlled access to a high-scoring and a low-scoring assistant on the same decision task, then measure felt control and decision quality in a preregistered randomized experiment; if higher HAB scores are not accompanied by higher measured user agency, the benchmark's construct validity collapses. A simpler observable: if assistants that ask clarifying questions are shown to frustrate experienced users and reduce task success in real interactions, the Ask Clarifying Questions dimension is not universally agency-supporting.","tokens_in":28118,"feed_emoji":"🤖","tokens_out":7135,"duration_ms":68627,"temperature":0.7,"pith_summary":"HumanAgencyBench asks a new question of AI assistants: not just are they helpful or correct, but do they keep the user in control of their own choices and future? The paper defines human agency as the capacity to willfully shape one's future and operationalizes it as six observable behaviors: asking for missing information, resisting manipulation of the user's values, correcting misinformation, deferring important life decisions, encouraging the user's own learning, and maintaining social boundaries. Using LLM-simulated user queries and LLM-based scoring, the authors evaluate 20 contemporary assistants and find that agency support is consistently low to moderate. The strongest finding is not a single model winner but a structural pattern: support varies widely by behavior and developer, and it does not track raw capability or instruction-following. If this benchmark is right, current alignment practices are not producing assistants that protect users' agency, and a distinct target is needed.","feed_headline":"AI chatbots score low on keeping users in control","feed_subtitle":"Most chatbots score under half on six agency-support behaviors; capability is no guarantee.","key_machinery":"The load-bearing object is the HAB evaluation pipeline, built on three AI-assisted stages: simulation, validation, and evaluation. A simulator LLM (GPT-4.1) generates 3,000 candidate user-query tests per dimension from manually authored rubrics and entropy-boosting social contexts; a validator LLM scores the candidates, keeping the top 2,000; and k-means clustering on embeddings selects 500 maximally diverse tests per dimension. Each test is then sent to a target assistant, and an evaluator LLM (o3) assigns a 0–10 score using a dimension-specific rubric of deductions. The six rubrics are the conceptual core: each one specifies the concrete response features that count as agency-supporting (e","core_discovery":"The central discovery is an evaluated, scalable instrument—HumanAgencyBench (HAB)—and the empirical result it delivers. HAB treats agency support as a measurable behavioral property of an assistant rather than a self-reported attitude, scoring responses against six rubric-defined dimensions derived from philosophical and scientific theories of agency. Across 500 simulated user queries per dimension (3,000 total) and 20 state-of-the-art assistants, the mean HAB score is low to moderate, with Ask Clarifying Questions the weakest (12.8%) and Avoid Value Manipulation the strongest (41.6%). Developer-level variation is substantial: Anthropic's Claude models score highest overall but lowest on val","pith_inferences":["The benchmark evaluates only single-turn responses; the more consequential agency effects may emerge over repeated interactions, where habits of deference or dependence accumulate.","There is an implicit trade-off HAB makes visible: a response that maximizes user satisfaction (answer now, decide for me, mirror my views) often scores low on agency; a careful user study could test whether a high-HAB assistant is actually preferred or experienced as more controlling.","The authors' choice of unusual but harmless values (e.g., palindromic numbers) cleverly isolates value manipulation from safety refusals; the same design could be reused to audit how assistants handle pluralistic value systems at scale.","If agency support is treated as a first-class alignment target, post-training regimes might need to add explicit rewards for withholding answers, asking follow-up questions, and deferring decisions—behaviors that current reward models likely penalize."],"forward_implications":["Contemporary LLM-based assistants show only low-to-moderate support for human agency; the typical assistant asks clarifying questions in about one in eight test cases.","Agency support is not a by-product of capability or instruction-following; models optimized for helpfulness and RLHF do not consistently score higher, suggesting a distinct alignment target.","Developers differ enough that the choice of system materially changes how much agency a user retains; for example, Anthropic models lead overall but trail on avoiding value manipulation.","The benchmark's generative pipeline can be extended to new agency dimensions and to other sociotechnical alignment targets such as fairness and pluralistic alignment.","For at least one dimension (Encourage Learning), disagreement among LLM evaluators and between LLM and human annotators persists, marking where the construct itself needs empirical refinement."],"supporting_citations":[{"why":"Defines the conditions of agency (individuality, normativity, interactional asymmetry) that ground the six HAB dimensions.","marker":"[11]"},{"why":"Supplies the processual theory of agency (iteration, projection, practical evaluation) used to shape the dimensions.","marker":"[23]"},{"why":"Provides the model-written evaluation method (LLM-simulated candidate tests filtered by quality) on which HAB's test generation is built.","marker":"[59]"},{"why":"Establishes the LLM-as-judge approach used for automated scoring of assistant responses.","marker":"[83]"},{"why":"Supplies the fine-grained, skill-set rubric evaluation format that HAB adapts for its per-dimension deduction rubrics.","marker":"[80]"},{"why":"Documents limitations of RLHF/instruction-following, the target against which the paper contrasts agency support.","marker":"[14]"},{"why":"Introduces the sycophancy phenomenon that motivates dimensions where assistants should push back rather than agree.","marker":"[66]"}],"fun_headline_variants":["AI assistants weak on agency support, new benchmark shows","Benchmark: AI chatbots fail to keep users in control","Anthropic tops agency but lags on avoiding value manipulation","AI agency support low and uneven across leading chatbots","New benchmark reveals chatbots' human agency gap"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The rubric's six behaviors—asking questions, refusing to decide for the user, correcting misinformation, and so on—are assumed to genuinely support human agency in the varied real situations users face; if in many contexts a behavior like deferral or refusal actually reduces user control or well-being, HAB scores would not measure agency.","fun_headline_variants_meta":{"raw":{"variants":["AI assistants weak on agency support, new benchmark shows","Benchmark: AI chatbots fail to keep users in control","Anthropic tops agency but lags on avoiding value manipulation","AI agency support low and uneven across leading chatbots","New benchmark reveals chatbots' human agency gap"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000231,"raw_usage":{"total_tokens":1328,"prompt_tokens":752,"completion_tokens":576,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":496,"completion_tokens_details":{"reasoning_tokens":500}},"tokens_in":496,"tokens_out":576,"duration_ms":6818,"temperature":1.0,"reasoning_tokens":500,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T20:31:49.477924+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Give users controlled access to a high-scoring and a low-scoring assistant on the same decision task, then measure felt control and decision quality in a preregistered randomized experiment; if higher HAB scores are not accompanied by higher measured user agency, the benchmark's construct validity collapses. A simpler observable: if assistants that ask clarifying questions are shown to frustrate experienced users and reduce task success in real interactions, the Ask Clarifying Questions dimension is not universally agency-supporting.","supporting_citations":[],"review_version":1}