Learning Simple Test-Time Environments for LLM Web Agents
Large language model (LLM) agents have demonstrated remarkable proficiency in manually constructed environments, yet their performance frequently collapses when transitioned to complex real-world settings. Existing research largely attribute this degradation to the compositional generalization gaps in LLMs on combinations of multiple simple, well-structured environments. In this work, we propose that LLM web agents can learn simple environment observations at test time. Specifically, we introduce trial steps for agents to decompose a complex environment observation into sub-modules, and implement a label-free learning method, Test-Time Environment Decomposition (TTED), to adapt agent beha...
| Authors | Junxuan Li, Zijun Liu, Ziyi Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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