arXiv:2606.12344v1 Announce Type: cross Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol that makes heterogeneous agent harnesses, or claws, comparable under fair settings including a fixed prompt, runtime budget, workspace contract, patch extraction procedure, and eval
The proliferation of general-purpose AI agents necessitates more robust and standardized benchmarks to accurately assess their capabilities beyond anecdotal evidence.
This benchmark provides a crucial quantitative method for comparing the rapidly evolving landscape of AI agents, particularly in complex coding tasks, which will accelerate their development and deployment.
The ability to objectively measure and compare different AI agent 'harnesses' allows for clearer progress tracking and more informed investment and application decisions in agentic AI.
- · AI agent developers
- · Companies adopting AI agents
- · Open-source AI communities
- · Companies with proprietary, unbenchmarkable AI agent solutions
- · Manual software testers
Claw-SWE-Bench will become a standard for evaluating AI agent performance on coding tasks.
Increased competition among AI agent developers, leading to faster innovation and more capable agents.
The benchmark could become a foundational component in certifications or regulatory frameworks for autonomous coding and software development by AI agents.
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Read at arXiv cs.CL