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Review the Episodes, Refine the Lessons: Simple Memory Evolution for Embodied Agents — arXiv:2611.11509

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.11509
License (as archived): CC0-1.0
Archived: 2026-11-10T18:43:27.483Z
SHA-256 of the archived text: 35cf08e536ab5c7b249dbee5ebf2493ce8fe5cd64776b51f0f80a7352d0f6da5
Stable URL of this page: https://metraipolis.com/documento/dc_72ea3d9affe1

How to cite

Review the Episodes, Refine the Lessons: Simple Memory Evolution for Embodied Agents — arXiv:2611.11509 — arXiv (en), https://arxiv.org/abs/2611.11509 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_72ea3d9affe1 (Metraipolis archive dc_72ea3d9affe1, sha256 35cf08e536ab5c7b249dbee5ebf2493ce8fe5cd64776b51f0f80a7352d0f6da5)

BibTeX

@misc{dc72ea3d9affe1,
  title = {Review the Episodes, Refine the Lessons: Simple Memory Evolution for Embodied Agents — arXiv:2611.11509},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.11509},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_72ea3d9affe1; sha256 35cf08e536ab5c7b249dbee5ebf2493ce8fe5cd64776b51f0f80a7352d0f6da5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.11509. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent studies of embodied agents based on frontier multimodal large language models (MLLMs) develop harness engineering around the MLLM, such as harnesses with post-trained policies and recursive self-improvement loops. Yet whether these additions exceed the native capability of the MLLM remains unexplored. We introduce SimplEvo to explore how far an MLLM can improve under direct control when it retains its execution experience in self-written text memories. It requires no weight updates, post-trained policies, or privileged information. The MLLM writes a coordinate-free task memory once from a single demonstration. Under a strict train-test protocol, it then creates a bounded working memory on training episodes only and refines it after each episode with lessons on procedure, completion, and recovery. Both memories stay fixed at test time. On EBench, SimplEvo lifts the success rate (SR) of GPT-6-Astra under direct control from 46.7% to 81.7%, and tops the RoboDojo leaderboard with 69.63% SR and a score of 75.32. Compared with memories that also store past visual frames, SimplEvo reaches a similar SR with text memory alone while using fewer tokens. With its memories fixed, SimplEvo also generalizes to unseen layouts and randomized scene configurations. Moreover, the working memory transfers across models: written by GPT-6-Astra, it can be reused as is by weaker MLLMs and improve their performance without any adaptation of their own.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.11509 — 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 2026-11-10T18:37:37.645Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).