AdaExploreBench
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- AdaExploreBench benchmarks LLM agents' proactive exploration
An arXiv paper posted on 19 October introduces AdaExploreBench, a benchmark from Rui Wang and 17 co-authors for proactive exploration and test-time adaptation in evolving environments. The benchmark encodes environment dynamics as hidden rules agents must actively gather evidence for and infer, and under implicit rule drift the latent rule changes without announcement, forcing agents to notice stale knowledge from degraded performance and re-explore. Across experiments on eight leading LLMs, the authors report that proactive, adaptive exploration remains difficult: GPT-6 Sol reaches only 61.9% calibrated accuracy, while supplying sufficient evidence or removing rule drift raises average performance by close to 10%.
Hugging Face · Papers · 🔥 0
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