arXiv AI
OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
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arXiv AI
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Thalamus-Reasoning: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
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.
arXiv Machine Learning
Sep 22
Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks
The paper introduces OmicsBench, a new reasoning benchmark for multi‑omics sequences that includes 1,160 expert‑validated questions across DNA regulation, RNA processing, and protein function tasks, requiring traceable evidence chains. Evaluation of 17 large language models shows that scientific LLMs, while more accurate in classification, often lack valid evidence, suggesting shortcut learning. To address this, the authors propose tool‑augmented on‑policy distillation (TA‑OPD), a post‑training method that improves both evidence grounding and predictive performance across five Qwen3.5 models of varying sizes.
arXiv AI
Jul 13
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.
arXiv Machine Learning
Oct 3
When Do Biological Reasoning Models Use Their Biological Inputs?
The study evaluates whether biological reasoning models actually use their biological inputs by testing six models on DNA, protein, and single‑cell tasks. By perturbing one biological input while keeping others fixed, the authors find that many models (e.g., Evo2, ESM3, BioReason, BioReason‑Pro) rely primarily on textual information, with minimal impact from the biological representations. In contrast, models like ChatNT, Prot2Text‑V2, CellWhisperer, and Cell2Sentence‑Scale show greater dependence on their biological inputs, yet overall accuracy gains do not consistently reflect increased biological input contribution.
arXiv AI
Aug 11
MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks
arXiv:2507. 19634v4 Announce Type: replace-cross Abstract: Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a single framework.
arXiv Machine Learning
Oct 2
Prototype Guided Post-pretraining for Single-Cell Representation Learning
arXiv:2605.07938v2 Announce Type: replace Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...