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arXiv AI By Maciej Sypetkowski, {\L}ukasz Smoli\'nski, Remigiusz Kinas, Joanna Krawczyk, Bart{\l}omiej Hofman, Aleksandra Mo\.zwi{\l}{\l}o, Micha{\l} Pikusa, Tomasz Jetka, Rafa{\l} Powalski

Thalamus-Reasoning: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

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Thalamus‑Reasoning is a 9B multimodal large language model that embeds bulk or single‑cell transcriptomes as continuous vectors, allowing them to be interleaved with natural‑language instructions and other samples. The model is trained on a large corpus that links experimental measurements to biological knowledge and task supervision, covering 157 task groups and 5.21 million conversations. It outperforms competing models on several held‑out annotation tasks, the GEO‑OmicsQA benchmark, and PerturbQA perturbation‑response prediction, demonstrating the benefit of continuous omics representations over text‑only gene lists.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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