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Shaping Persistent Representations from Independent Interactions — arXiv:2609.34604

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34604
License (as archived): CC0-1.0
Archived: 2026-10-07T22:38:27.599Z
SHA-256 of the archived text: a11c33dd49ef179b9c328a91f74a4144ccc36d481e9d668e1b5155c7eea641bd
Stable URL of this page: https://metraipolis.com/documento/dc_8b60cb8ff11b

How to cite

Shaping Persistent Representations from Independent Interactions — arXiv:2609.34604 — arXiv (en), https://arxiv.org/abs/2609.34604 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8b60cb8ff11b (Metraipolis archive dc_8b60cb8ff11b, sha256 a11c33dd49ef179b9c328a91f74a4144ccc36d481e9d668e1b5155c7eea641bd)

BibTeX

@misc{dc8b60cb8ff11b,
  title = {Shaping Persistent Representations from Independent Interactions — arXiv:2609.34604},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34604},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8b60cb8ff11b; sha256 a11c33dd49ef179b9c328a91f74a4144ccc36d481e9d668e1b5155c7eea641bd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34604. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

World models learn environment dynamics from interaction experience. These dynamics depend on the current state and actions, as well as on properties that persist across interactions. Yet standard predictive training can reduce error using local evidence alone, without organizing persistent information into reusable context. We introduce SPRII, a training principle that uses relations between interactions as weak supervision for persistent context while retaining the learner's native objective. For example, different trajectories of the same system share persistent properties even when their states and actions differ. SPRII uses such relations to guide context learning without numerical property labels. Two composable components encourage contexts from related interactions to agree (Align) and use one interaction's context to predict another's future (Cross). Our analysis distinguishes three linked questions: what persistent information is accessible in the learned context (Formation), how that context influences a fixed predictor (Use), and whether it reduces task error (Value). Success at one stage does not guarantee success at the next. Controlled experiments show that more reliable relations improve representation organization, but adding a shared-property constraint can reduce access to a property that remains shared. Context substitutions change predictions at fixed model weights, while the benefit from history depends on prediction horizon and readout. Evaluations span thirteen settings, including controlled physical systems, public dynamics tasks, robotic and tactile data, and partner interaction, across multiple learner families. Relative to the corresponding baselines, SPRII yields average gains of over 10% in downstream task performance and over 15% in persistent-property readout. The project page is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.34604 — 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 2026-10-07T22:14:28.242Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).