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. Evaluating these assistants is fundamentally a fidelity problem: benchmarks must be faithful both to the distribution of real assistant tasks and to the execution semantics of the environments in which those tasks unfold. Existing benchmarks often lose fidelity in one dimension or the other. Their task distributions are shaped by what is easy to isolate, mock, and verify, underrepresenting real-wor
The rapid advancement and deployment of LLM agents for increasingly complex tasks necessitate effective benchmarking to ensure real-world utility and reliability.
Accurate benchmarking is crucial for guiding the development and safe deployment of AI agents, directly impacting their commercial viability and societal integration.
The proposed 'LiveClawBench' aims to provide a more faithful evaluation of LLM agents, shifting development focus towards real-world task fidelity rather than isolated capabilities.
- · AI agent developers
- · Companies adopting AI assistants
- · Benchmarking platforms
- · Developers relying on easy-to-mock benchmarks
- · Systems with poor real-world task performance
Improved performance and reliability of LLM-powered personal assistants.
Accelerated adoption and integration of AI agents into white-collar workflows, leading to new software paradigms.
Increased competition among AI companies to demonstrate superior agentic capabilities in complex, real-world scenarios.
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Read at arXiv cs.LG