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Sensor-Language-Action Models — arXiv:2610.08244

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08244
License (as archived): CC0-1.0
Archived: 2026-10-07T11:19:34.184Z
SHA-256 of the archived text: eb72bb87019901c18196628d0849467e269bec38283c2fa62769a63bdcf6d133
Stable URL of this page: https://metraipolis.com/documento/dc_74716de784ff

How to cite

Sensor-Language-Action Models — arXiv:2610.08244 — arXiv (en), https://arxiv.org/abs/2610.08244 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_74716de784ff (Metraipolis archive dc_74716de784ff, sha256 eb72bb87019901c18196628d0849467e269bec38283c2fa62769a63bdcf6d133)

BibTeX

@misc{dc74716de784ff,
  title = {Sensor-Language-Action Models — arXiv:2610.08244},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08244},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_74716de784ff; sha256 eb72bb87019901c18196628d0849467e269bec38283c2fa62769a63bdcf6d133},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08244. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Sensors are useful not only for understanding the world but also for deciding what to do next. Existing sensor models however largely stop at perception: they recognize states or predict outcomes, leaving actions modeled separately through task-specific and often closed label spaces. We introduce Sensor-Language-Action (SLA) modeling, a framework that connects multimodal sensor observations, natural language, and actions within a unified model. SLA uses language as a semantic interface between sensing and acting, allowing heterogeneous actions to be represented, predicted, and explained while remaining grounded in the underlying sensor evidence. We build a large-scale SLA benchmark consisting of datasets that span more than 116,000 individuals, 79 sensor modalities, and 60 action groups, together with a multi-faceted captioning pipeline that aligns user context, sensor dynamics, and action evidence. Building on this framework, we present OpenSLA, a unified SLA model for hierarchical action prediction, state understanding, and action explanation. Extensive experiments on real-world tasks in clinical prediction, operating rooms, and metabolic health verify its superior performance over the state-of-the-art. OpenSLA also demonstrates intriguing capabilities including language-guided evidence grounding and zero-shot generalization to unseen actions and cohorts.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.08244 — 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 2026-10-07T11:15:24.599Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).