AdaExploreBench tests LLM agents on exploration and rule drift
Original title: AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
Across eight leading models, GPT-6 Sol reaches only 61.9% calibrated accuracy when rules stay hidden and can change.
AdaExploreBench measures whether LLM agents can discover hidden environmental rules and adapt when those rules drift without notice. Agents must gather evidence, infer the rules, and re-explore after performance degrades. Experiments on eight leading LLMs find proactive evidence gathering and stale-knowledge detection remain hard. Giving enough evidence or removing drift lifts average performance by close to 10%.
- Hidden rules force agents to collect evidence rather than receive the dynamics.
- Implicit rule drift is unannounced and must be inferred from worse results.
- Sufficient evidence or no drift raises average scores by nearly 10%.
- Authors say agents reason well once informative evidence is already available.
From the original article
1 min read at huggingface.co
Large Language Model (LLM) agents are increasingly deployed in unfamiliar and evolving environments, requiring them to proactively explore, understand, and adapt to environmental dynamics. However, existing benchmarks rarely test whether agents can proactively discover how an environment works and adapt as it evolves.
By Rui Wang, Zishen Lai, Jiayu Liu · huggingface.co
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