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arxiv: 2605.10365 · v1 · submitted 2026-05-11 · 💻 cs.AI

Recognition: 1 theorem link

· Lean Theorem

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

Haonan Dong , Qiguan Feng , Kehan Jiang , Haoran Ye , Xin Zhang , Guojie Song
Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:18 UTC · model grok-4.3

classification 💻 cs.AI
keywords agent valuesvalue alignmentAI agentsharnessesskill steeringvalue systemsAI safetybenchmarks
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The pith

Agents display values distinct from their base LLMs that form a cross-model Value Tide malleable by harnesses and skills.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to demonstrate that autonomous agents hold value systems separate from those of the language models powering them, and that the shift to agentic execution creates new measurement problems not addressed by existing text-only value tests. It builds Agent-ValueBench to fill this gap, supplying executable environments, value-conflict tasks, and psychologist-curated golden trajectories that let evaluators score real agent behavior rather than static answers. Benchmark runs across models and harnesses reveal a broad homogeneity in agent values alongside clear non-additive shifts when harnesses change or skills are embedded. A reader would care because these patterns point to where control over agent behavior actually resides once models are deployed in harnesses rather than in the models themselves.

Core claim

The central claim is that agent values diverge from those of the underlying LLM and manifest as a Value Tide of cross-model homogeneity; this tide bends non-additively when different harnesses are applied and bends more decisively when skills are deliberately embedded, which together indicate that the practical lever for agent alignment is moving from model-level and prompt-level methods toward harness alignment and skill steering.

What carries the argument

Agent-ValueBench, the benchmark of 394 executable environments across 16 domains that supplies 4,335 value-conflict tasks covering 28 value systems and 332 dimensions, each equipped with two pole-aligned golden trajectories scored by a trajectory-level rubric judge.

Load-bearing premise

The end-to-end synthesis pipeline together with psychologist curation produces tasks and golden trajectories that measure the intended 28 value systems and 332 dimensions without systematic artifacts from generation or expert judgment.

What would settle it

Running the same 14 models on a fresh, independently authored collection of executable value-conflict tasks and finding that agent values match the base LLM values exactly while showing no measurable change under harness swaps or skill insertion would falsify the divergence and non-additive bending claims.

read the original abstract

Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly drawn growing research attention, and beneath them lie the values silently steering agent behavior. Existing value benchmarks, however, remain confined to LLMs, leaving agent values largely uncharted. From intuitive, empirical, and theoretical vantage points, we show that an agent's values diverge from those of its underlying LLM, and the agentic modality further introduces dataset-, evaluation-, and system-level challenges absent from text-only protocols. We close this gap with Agent-ValueBench, the first benchmark dedicated to agent values. It features 394 executable environments across 16 domains, offering 4,335 value-conflict tasks that cover 28 value systems and 332 dimensions. Every instance is co-synthesized through our purpose-built end-to-end pipeline and curated per-instance by professional psychologists. Each task ships with two pole-aligned golden trajectories whose checkpoints anchor a trajectory-level rubric-based judge. Benchmarking 14 frontier proprietary and open-weights models across 4 mainstream harnesses, we uncover three concerted findings. Agent values first manifest as a Value Tide of cross-model homogeneity beneath interpretable counter-currents. This tide bends non-additively under harness pull, and yet more decisively under deliberate steering via embedded skills. Together these results signal that the agent-alignment lever is shifting from classical model alignment and prompt steering toward harness alignment and skill steering.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces Agent-ValueBench as the first dedicated benchmark for evaluating values in autonomous agents. It constructs 4,335 value-conflict tasks across 394 executable environments in 16 domains, covering 28 value systems and 332 dimensions. Tasks are generated via a purpose-built end-to-end synthesis pipeline and per-instance curated by professional psychologists, each accompanied by two pole-aligned golden trajectories for a trajectory-level rubric-based judge. Benchmarking 14 frontier models across 4 harnesses yields three findings: agent values exhibit a 'Value Tide' of cross-model homogeneity distinct from underlying LLMs; this homogeneity bends non-additively under harness influence; and it is further modulated by embedded skill steering. The work argues that these results shift the agent-alignment focus from model-level to harness- and skill-level interventions.

Significance. If the synthesis pipeline and curation produce artifact-free measures, the benchmark would represent a substantial advance in AI safety and alignment research by extending value evaluation beyond text-only LLM protocols to agentic settings. The empirical demonstration of value divergence, cross-model homogeneity, and non-additive modulation by harnesses and skills provides concrete evidence for new alignment levers and highlights dataset-, evaluation-, and system-level challenges unique to agents. The inclusion of executable environments, golden trajectories, and a rubric-based judge supports reproducibility and falsifiability, strengthening the contribution if methodological validity is established.

major comments (3)
  1. [Benchmark Construction] Benchmark Construction section: The end-to-end synthesis pipeline and per-instance psychologist curation are described at a high level but provide no details on validation procedures, inter-rater reliability statistics, or controls for systematic biases (e.g., embedding of LLM priors in task generation or selection effects in the 4,335 tasks). This is load-bearing for the central claims of value divergence and the Value Tide, as any artifact in task instantiation of the 28 value systems would render the homogeneity and steering results non-generalizable.
  2. [Experimental Setup and Results] Experimental Setup and Results section: The reported cross-model homogeneity and non-additive bending under harness pull and skill steering lack explicit statistical controls, significance testing, or ablation details on how dataset-, evaluation-, and system-level factors were isolated. Without these, it is unclear whether the Value Tide findings hold after accounting for potential confounds in the 14-model, 4-harness evaluation.
  3. [Evaluation Methodology] Evaluation Methodology subsection: The trajectory-level rubric-based judge, anchored by golden trajectories, is presented without reported validation against human judgments or sensitivity analysis to variations in harness implementation. This directly affects the reliability of the value measurements underlying all three main findings.
minor comments (2)
  1. [Abstract] Abstract: The novel term 'Value Tide' is used without a concise definition or forward reference, reducing immediate clarity for readers.
  2. [Results] Figure and Table captions: Several result visualizations would benefit from explicit legends distinguishing the 28 value systems and the four harnesses to aid interpretation of the homogeneity patterns.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback and recommendation for major revision. The comments highlight important opportunities to increase methodological transparency. We address each major comment point-by-point below and will incorporate the requested details, statistical analyses, and validation results into the revised manuscript to strengthen the evidence for our findings on agent values.

read point-by-point responses

  1. Referee: [Benchmark Construction] Benchmark Construction section: The end-to-end synthesis pipeline and per-instance psychologist curation are described at a high level but provide no details on validation procedures, inter-rater reliability statistics, or controls for systematic biases (e.g., embedding of LLM priors in task generation or selection effects in the 4,335 tasks). This is load-bearing for the central claims of value divergence and the Value Tide, as any artifact in task instantiation of the 28 value systems would render the homogeneity and steering results non-generalizable.

    Authors: We agree that expanded details on validation and bias controls are necessary to support the benchmark's claims. The manuscript currently summarizes the pipeline and per-instance psychologist curation at a high level for conciseness. In the revision, we will substantially expand the Benchmark Construction section to report inter-rater reliability statistics (e.g., Fleiss' kappa across psychologists for value dimension assignments and task curation), explicit procedures for mitigating LLM priors (including multi-stage human-only review of generated tasks), and analysis of selection effects to confirm balanced coverage of the 28 value systems. Full curation protocols and bias-control documentation will be added to the supplementary materials. revision: yes

  2. Referee: [Experimental Setup and Results] Experimental Setup and Results section: The reported cross-model homogeneity and non-additive bending under harness pull and skill steering lack explicit statistical controls, significance testing, or ablation details on how dataset-, evaluation-, and system-level factors were isolated. Without these, it is unclear whether the Value Tide findings hold after accounting for potential confounds in the 14-model, 4-harness evaluation.

    Authors: We acknowledge that additional statistical rigor will better isolate the Value Tide from potential confounds. The current results demonstrate consistent patterns across 14 models and 4 harnesses, but we will revise the Experimental Setup and Results section to include formal significance testing (e.g., ANOVA with post-hoc tests and reported p-values/effect sizes for cross-model homogeneity), ablation studies that systematically vary dataset, evaluation, and system factors, and controls for confounds such as model scale or harness-specific artifacts. Updated figures and tables will show that the homogeneity and non-additive bending persist after these adjustments. revision: yes

  3. Referee: [Evaluation Methodology] Evaluation Methodology subsection: The trajectory-level rubric-based judge, anchored by golden trajectories, is presented without reported validation against human judgments or sensitivity analysis to variations in harness implementation. This directly affects the reliability of the value measurements underlying all three main findings.

    Authors: We appreciate this point on evaluation reliability. The golden trajectories anchor the rubric-based judge, but external validation was not detailed in the original submission. In the revised manuscript, we will augment the Evaluation Methodology subsection with a human validation study on a stratified subset of trajectories (reporting agreement metrics such as Cohen's kappa between the automated judge and professional evaluators) and a sensitivity analysis testing variations in harness implementations (e.g., alternative prompt templates and environment configurations). Full results and methodology for these checks will appear in the main text and appendix. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark construction and observations are independent of inputs

full rationale

The paper introduces Agent-ValueBench through an end-to-end synthesis pipeline and per-instance psychologist curation to generate 4,335 tasks covering 28 value systems, then reports direct empirical results from running 14 models across 4 harnesses. No equations, parameter fitting, derivations, or self-referential predictions appear in the presented claims. The Value Tide homogeneity, non-additive harness effects, and skill steering findings are observational outputs from the benchmark rather than quantities forced by construction or prior self-citations. The construction pipeline is described as purpose-built and externally curated, with no reduction of results to the generation process itself.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on domain assumptions about value measurability via trajectories and standard psychological value frameworks; no free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption Agent values can be reliably measured and distinguished through choices in executable environments using golden trajectories and rubric-based judging.
    Invoked in the benchmark design and evaluation protocol described in the abstract.
  • domain assumption Professional psychologist curation ensures validity of value-conflict tasks across 28 systems and 332 dimensions.
    Stated as part of the co-synthesis and curation process.

pith-pipeline@v0.9.0 · 5570 in / 1363 out tokens · 32795 ms · 2026-05-12T04:18:10.344344+00:00 · methodology

discussion (0)

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