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TriPhysBench: Evaluating Physical Intelligence for Embodied Agents — arXiv:2611.01934

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01934
License (as archived): CC0-1.0
Archived: 2026-11-03T10:43:39.297Z
SHA-256 of the archived text: 36432b267e01691cc6373f80d223295ed4940c1cd3bdb9089a6fc3c3a36cb5f0
Stable URL of this page: https://metraipolis.com/documento/dc_ca97fc3aa152

How to cite

TriPhysBench: Evaluating Physical Intelligence for Embodied Agents — arXiv:2611.01934 — arXiv (en), https://arxiv.org/abs/2611.01934 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca97fc3aa152 (Metraipolis archive dc_ca97fc3aa152, sha256 36432b267e01691cc6373f80d223295ed4940c1cd3bdb9089a6fc3c3a36cb5f0)

BibTeX

@misc{dcca97fc3aa152,
  title = {TriPhysBench: Evaluating Physical Intelligence for Embodied Agents — arXiv:2611.01934},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01934},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca97fc3aa152; sha256 36432b267e01691cc6373f80d223295ed4940c1cd3bdb9089a6fc3c3a36cb5f0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01934. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Embodied agents need physical intelligence to understand their surroundings through physical perception, physical knowledge retrieval, and physical reasoning. These capabilities must work together to interpret events and make quantitative judgments. Existing benchmarks advance physical understanding and general-purpose tool use, yet the coordination of these capabilities in physical scenarios remains insufficiently characterized. Inspired by Popper's Three Worlds, we introduce TriPhysBench, a benchmark of 410 questions from 200 videos that brings physical perception, physical knowledge retrieval, and physical reasoning into a shared evaluation setting. Its measurement-grounded and knowledge-grounded subtasks impose complementary demands on obtaining observations and acquiring external specifications, then applying them to quantitative problems. Among six representative vision-language models, the best achieves an answer accuracy of only 50.38%, revealing limited performance on tasks that require these capabilities to work together. We further introduce TriPhys, a framework that learns a unified policy for physical perception, knowledge retrieval, and quantitative reasoning from complete tool-integrated trajectories. The policy uses intermediate reasoning to guide retrieval and computation, then incorporates tool feedback into subsequent physical inference. On 110 questions from videos excluded from training, TriPhys improves answer and solution scores across three Qwen configurations, with a 17.7% relative gain in answer accuracy for TriPhys-27B over its Qwen3.6-27B backbone. This work provides a benchmark and a learning pathway toward embodied agents that move beyond recognizing events to understanding and reasoning about the physical processes behind them.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.01934 — 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 2026-11-03T10:39:50.249Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).