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LegoEnv: Self-Evolving Agentic Environments via Modular Recombination — arXiv:2611.01834

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01834
License (as archived): CC0-1.0
Archived: 2026-11-03T10:47:55.385Z
SHA-256 of the archived text: 6e6d3a09f14ac55d284f0b1b11eeea14460a50bae595ff238e944725357ed7bf
Stable URL of this page: https://metraipolis.com/documento/dc_692d91c00173

How to cite

LegoEnv: Self-Evolving Agentic Environments via Modular Recombination — arXiv:2611.01834 — arXiv (en), https://arxiv.org/abs/2611.01834 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_692d91c00173 (Metraipolis archive dc_692d91c00173, sha256 6e6d3a09f14ac55d284f0b1b11eeea14460a50bae595ff238e944725357ed7bf)

BibTeX

@misc{dc692d91c00173,
  title = {LegoEnv: Self-Evolving Agentic Environments via Modular Recombination — arXiv:2611.01834},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01834},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_692d91c00173; sha256 6e6d3a09f14ac55d284f0b1b11eeea14460a50bae595ff238e944725357ed7bf},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01834. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

LLM agents learn by interacting with environments, yet existing environments are designed for specific tasks and fail to generalize to new ones. Recent methods automate environment construction with LLMs. However, they generate each environment from scratch, neither reusing parts shared across environments nor carrying a fix from one environment to the rest. To address this, we propose a new paradigm of evolving environment atoms, which composes environments from reusable atoms (intent, rule, data and server) and evolves the atoms when an environment fails to give reliable feedback (e.g., an application lacks a tool the task requires). To instantiate this paradigm, we introduce LegoEnv, which builds 5,465 verified environments over 223 applications, and one round of evolution raises the share of solvable environments from 71.6% to 88.3%. On AutomationBench, SFT on these environments lifts Qwen3.6-27B from 16.0% to 32.0%, and RL with their verifiers lifts Qwen3.8-27B from 37.6% to 40.3%. Further analyses show steady gains with corpus size, transfer to unseen applications, and failures concentrated in a few shared root causes.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01834 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:39:50.515Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).