arXiv (All)
open access
Breaking the Environment Wall: Evolving LLM Agent Environments for Recursive Self-Improvement
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 environ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?#1071224
- Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning#1034764
- AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement#471221
- Dream-RSI: Recursive Self-Improvement through Evolving Worlds#1084772
- How do World Models and Policies Compose in LLM Agents? A Joint Spectral and Behavioral Account#799304
Record · ID 1077700
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive