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ARXIV:2605.10365 · AGENT VALUES · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.10365AGENT VALUESSUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHHaonan Dong · Qiguan Feng · Kehan Jiang · Haoran Ye · Xin Zhang · Guojie Song · arXiv
Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill…
Opportunity summary
Pain Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering. Safety concerns have…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Agent Values moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill…
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10.48550/arXiv.2605.10365Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering.
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.
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Dimensions overall score 7.0
PROBLEM
Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering. Safety concerns have rightly drawn growing research...
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 s...
WHY NOW
Agent Values moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering. Safety concerns have rightly drawn growing research attention, and beneath them lie the values silently steering agent behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agent Values moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Agent-ValueBench, the first benchmark for evaluating agent values, features 394 environments and 4,335 value-conflict tasks, revealing that agent values diverge from LLM values and are influenced by harness and skill steering.
Segment
Agent Values
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7.0/10 public viability
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reason
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passport absent
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Artifact maturity
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fresh
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Technical feasibility
partial
Current read
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Gaps
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missing
Current read
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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