arXiv AI
ClawProBench: Trace-Aware Evaluation of AI Agents with Runtime Coverage and Frozen Workplace-Style Holdouts
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arXiv AI
Jun 29
LiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
arXiv:2604. 13072v2 Announce Type: replace-cross Abstract: OpenClaw-style personal assistants extend LLM agents from isolated tool use to open-ended, stateful, and personalized software environments.
arXiv AI
Jul 1
An Executable Benchmarking Suite for Tool-Using Agents
arXiv:2605. 11030v2 Announce Type: replace-cross Abstract: Closed-loop tool-using agents are increasingly evaluated in executable web, code, and micro-task environments, but benchmark reports often conflate workloads, action-generating drivers, and the evidence admitted for systems-facing claims.
arXiv Machine Learning
Jul 31
ClawTrack: Towards Trace-Level Evaluation and Improvement of Real-World Autonomous Agents
arXiv:2607. 28037v1 Announce Type: new Abstract: As LLM-based agents are deployed in complex, multi-step workflows, a critical evaluation gap has emerged: most existing benchmarks judge only final outcomes, unable to distinguish reliable reasoning from lucky success or attribute failures to specific process deficiencies, hindering attribution in long-horizon tasks.
arXiv AI
Jul 21
ClawBench: Can AI Agents Complete Everyday Online Tasks?
arXiv:2604. 08523v2 Announce Type: replace-cross Abstract: AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites?
arXiv AI
Jul 15
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
arXiv:2606. 29537v2 Announce Type: replace Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents.
arXiv AI
Aug 7
SkillTV-Bench: Benchmarking How Well Judges Perform on Skill-Augmented Agentic Execution
arXiv:2608. 05573v1 Announce Type: new Abstract: LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions.