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ARXIV:2604.06132 · AUTONOMOUS AGENTS · SUBMITTED 08 APR · 03:21 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06132AUTONOMOUS AGENTSSUBMITTED 08 APR · 03:21 UTCFRESHNESS UNKNOWNBowen Ye · Rang Li · Qibin Yang · Yuanxin Liu · Linli Yao · Hanglong Lv · +7 at arXiv
Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment.
Opportunity summary
Pain Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment.
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Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2)…
Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2)…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Beyond benchmarking, Claw-Eval highlights actionable directions for agent development, shedding light on what it takes to build agents that are not only capable but…
Autonomous Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
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Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment.
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10.48550/arXiv.2604.06132Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment.
Abstract
Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2) underspecified safety and robustness evaluation, and (3) narrow modality coverage and interaction paradigms. We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps. It comprises 300 human-verified tasks spanning 9 categories across three groups (general service orchestration, multimodal perception and generation, and multi-turn professional dialogue). Every agent action is recorded through three independent evidence channels (execution traces, audit logs, and environment snapshots), enabling trajectory-aware grading over 2,159 fine-grained rubric items. The scoring protocol evaluates Completion, Safety, and Robustness, reporting Average Score, Pass@k, and Pass^k across three trials to distinguish genuine capability from lucky outcomes. Experiments on 14 frontier models reveal that: (1) trajectory-opaque evaluation is systematically unreliable, missing 44% of safety violations and 13% of robustness failures that our hybrid pipeline catches; (2) controlled error injection primarily degrades consistency rather than peak capability, with Pass^3 dropping up to 24% while Pass@3 remains stable; (3) multimodal performance varies sharply, with most models performing poorer on video than on document or image, and no single model dominating across all modalities. Beyond benchmarking, Claw-Eval highlights actionable directions for agent development, shedding light on what it takes to build agents that are not only capable but reliably deployable.
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PROBLEM
Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2)...
METHOD
Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2) u...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Beyond benchmarking, Claw-Eval highlights actionable directions for agent development, shedding light on what it takes to build agents that are not only capable but reliably deployable. Code availability...
WHY NOW
Autonomous Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2) underspecified safety and robustness evaluation, and (3) narrow modality coverage and interaction paradigms.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models are increasingly deployed as autonomous agents executing multi-step workflows in real-world software environments. However, existing agent benchmarks suffer from three critical limitations: (1) trajectory-opaque grading that checks only final outputs, (2) underspecified safety and robustness evaluation, and (3) narrow modality coverage and interaction paradigms.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Beyond benchmarking, Claw-Eval highlights actionable directions for agent development, shedding light on what it takes to build agents that are not only capable but reliably deployable. 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
Autonomous Agents moved forward this cycle; last verified April 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Claw-Eval: A trustworthy evaluation suite for autonomous agents that provides trajectory-aware grading, safety, and robustness assessment.
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