AISEApr 16

Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-CodeX

arXiv:2604.1485888.11 citationsh-index: 6
Predicted impact top 23% in AI · last 90 daysOriginality Synthesis-oriented
AI Analysis

This work provides extensible benchmarks for safety evaluation of agent trajectories, addressing the need for domain-specific risk coverage in diverse execution settings.

The paper introduces ATBench-Claw and ATBench-CodeX, two domain-specific extensions of the ATBench benchmark for evaluating trajectory safety in agent systems, tailored to OpenClaw and OpenAI Codex/Codex-runtime settings. The extensions customize a three-dimensional safety taxonomy to define benchmark specifications, enabling systematic safety evaluation across evolving execution environments.

As agent systems move into increasingly diverse execution settings, trajectory-level safety evaluation and diagnosis require benchmarks that evolve with them. ATBench is a diverse and realistic agent trajectory benchmark for safety evaluation and diagnosis. This report presents ATBench-Claw and ATBench-CodeX, two domain-customized extensions that carry ATBench into the OpenClaw and OpenAI Codex / Codex-runtime settings. The key adaptation mechanism is to analyze each new setting, customize the three-dimensional Safety Taxonomy over risk source, failure mode, and real-world harm, and then use that customized taxonomy to define the benchmark specification consumed by the shared ATBench construction pipeline. This extensibility matters because agent frameworks remain relatively stable at the architectural level even as their concrete execution settings, tool ecosystems, and product capabilities evolve quickly. Concretely, ATBench-Claw targets OpenClaw-sensitive execution chains over tools, skills, sessions, and external actions, while ATBench-CodeX targets trajectories in the OpenAI Codex / Codex-runtime setting over repositories, shells, patches, dependencies, approvals, and runtime policy boundaries. Our emphasis therefore falls on taxonomy customization, domain-specific risk coverage, and benchmark design under a shared ATBench generation framework.

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