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Breaking the Environment Wall: Evolving LLM Agent Environments for Recursive Self-Improvement

arXiv · AI, language, vision and robotics · article · Sep 24, 2026 · UTC

Many real-world tasks (e.g., office workflows, scientific experimentation) require LLM agents to interact repeatedly with their environments for context-dependent operations. However, such environments are often not agent-ready. First, information is often scattered and fragmented across the environment. Second, relevant evidence in the environment is often mixed with misleading information and conflicting versions. Third, environments evolve over time, introducing new noise and more challenging tasks. These challenges can substantially degrade performance for state-of-the-art AI agents (e.g.,

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