A hand-curated 67-task multilingual long-horizon agent benchmark finds large domain, language, and harness-driven performance gaps for current LLM agents.
Agentbench: Evaluating llms as agents
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Interactive evaluation of AI must be reframed as a distinct paradigm that maps interaction trajectories to judgments on process, recoverability, coordination, robustness, and system performance, supported by a two-axis taxonomy and design principles.
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PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
A hand-curated 67-task multilingual long-horizon agent benchmark finds large domain, language, and harness-driven performance gaps for current LLM agents.
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Interactive Evaluation Requires a Design Science
Interactive evaluation of AI must be reframed as a distinct paradigm that maps interaction trajectories to judgments on process, recoverability, coordination, robustness, and system performance, supported by a two-axis taxonomy and design principles.