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BrainCoT: A Multi-Task Zero-Shot Brain Signal Foundation Model with Neurometric-Anchored Chain-of-Thought Reasoning — arXiv:2611.09082

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.09082
License (as archived): CC0-1.0
Archived: 2026-11-09T22:41:25.776Z
SHA-256 of the archived text: fdc6e9a6deac8da81220d1f13c0814471b67702aeb707ff929ec87a45ee27eda
Stable URL of this page: https://metraipolis.com/documento/dc_30bf090e2fe2

How to cite

BrainCoT: A Multi-Task Zero-Shot Brain Signal Foundation Model with Neurometric-Anchored Chain-of-Thought Reasoning — arXiv:2611.09082 — arXiv (en), https://arxiv.org/abs/2611.09082 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_30bf090e2fe2 (Metraipolis archive dc_30bf090e2fe2, sha256 fdc6e9a6deac8da81220d1f13c0814471b67702aeb707ff929ec87a45ee27eda)

BibTeX

@misc{dc30bf090e2fe2,
  title = {BrainCoT: A Multi-Task Zero-Shot Brain Signal Foundation Model with Neurometric-Anchored Chain-of-Thought Reasoning — arXiv:2611.09082},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.09082},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_30bf090e2fe2; sha256 fdc6e9a6deac8da81220d1f13c0814471b67702aeb707ff929ec87a45ee27eda},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.09082. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Brain signals exhibit substantial multi-center heterogeneity, strong inter-subject variability, and highly diverse task settings, which makes it difficult to build models that generalize beyond a single task. While recent brain signal foundation models leverage large-scale pretraining to learn generalizable representations, they often fall short in practice because they still require target-data-specific supervised adaptation, lack multi-task zero-shot capability, and rarely provide verifiable evidence to support their predictions. In this work, we present BrainCoT, a multi-task zero-shot Brain signal foundation model with neurometric-anchored Chain-of-Thought (CoT) reasoning. BrainCoT unifies heterogeneous tasks into an instruction-driven framework so that, after pretraining, a single model can be directly applied to multiple tasks in zero-shot settings without additional adaptation. It further introduces neurometric-anchored chain-of-thought grounded in neurometric evidence for verifiable reasoning, and adopts Decision-Consistent Rationale Training to keep process learning aligned with correct predictions. Across extensive evaluations, BrainCoT achieves an average ACC of 67.41% and AUC of 74.33% in zero-shot classification without downstream training data, outperforming the strongest linear-probing baseline trained with 1% labeled downstream data by 3.85% in ACC and 5.97% in AUC.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.09082 — 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 2026-11-09T22:33:47.151Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).