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01Discovery · Microsoft Research AI for Science
AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the teams of scientists using them. With the announcement, Microsoft opened applications for the first Quine Fellows cohort and reported that, working with researchers at the Broad Institute of MIT and Harvard, it has used the system to rank compounds predicted to produce therapeutic shifts in tumor cell state, with several top-ranked candidates validated across multiple wet-lab assays. The announcement is authored by Nicolo Fusi, VP and distinguished scientist, and Jonathan M. Carlson, vice president. A World Model Built Across Biological Scales Microsoft defines the world model it is pursuing as one that represents the state of a biological system, forecasts how that state would change under an intervention, and reasons through the downstream consequences several steps ahead. The company says such a system would not replace experimentation; instead, it would let researchers weigh and rank candidate directions computationally before committing limited laboratory resources. Quine’s core model is trained to learn representations shared across biological scales and data types, among them sequence, structure, function, cellular state, and imaging. Its training modalities span genomics, proteins, chemistry, RNA and cell state, and bioimaging. Microsoft reports that joint training across these modalities strengthens rather than dilutes performance: evidence from one modality can inform predictions in another, capturing relationships the company says could stay siloed or be lost if separate single-domain specialist models were orchestrated instead. In Microsoft’s description, the system runs as a cycle that starts and ends with the scientist: a question yields proposals, proposals turn into experiments worth running, and the resulting measurements refine both the next question and the model itself. The model need not be perfect, the company says, adding that it will never perfectly capture biology; it needs only to guide experimental design usefully. The Qu…
Nature Portfolio · Nature Methods
Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell data. CytoVI generates informative cell embeddings, imputes missing measurements, performs differential protein expression testing and automates annotation of cells in a single probabilistic model. We applied CytoVI to build an integrated B cell maturation atlas spanning 350 proteins and identified proteins associated with immunoglobulin class-switching. In a cohort of patients with B cell non-Hodgkin lymphoma profiled by flow cytometry and CITE-seq, CytoVI uncovered disease-associated T cell states. CytoVI is available as open-source software at scvi-tools.org .
Hugging Face Daily Papers
Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can learning to fold proteins teach general models reusable reasoning capabilities? To answer this, we build FoldingCorpus, a protein-derived question-answer dataset, and Fold2Reason, a recipe that post-trains on it through two complementary signals: discrete structural answers predicted via the model's native language head, and continuous 3D geometry decoded from the same shared representations. On FoldBench, Fold2Reason achieves structure prediction scores 2.7 to 3.5 times those of Qwen3.5-9B. Beyond protein structure prediction, it improves performance on all 10 benchmarks spanning spatial, graph, scientific, and general reasoning, raising macro-average accuracy from 45.09% to 48.33% (+3.23 pp), with positive gains on all 10 benchmarks, while matched controls built from random, synthetic, and shuffled structure yield substantially smaller or negative gains. Our work shows that non-linguistic, structure-dense scientific data can systematically improve broad reasoning in language models, making a solved scientific problem a practical source of post-training supervision.
Hugging Face Daily Papers
LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic predictions over finite fields, but require those fields to be specified in advance. This requirement limits autonomous task solving, where the available actions must be derived from natural language instructions and adapted through interaction. We introduce JevSpawn, a compositional policy that connects natural language task specifications to finite probabilistic exploration. Parallel action spawning is coupled with feedback driven branch selection, representation revision, and recovery from retained alternatives. Shared action structure and model prefixes reduce repeated generation and context computation without additional training. Evaluations on eight benchmark tasks against seven agent baselines and a TypeSafe Jev variant establish JevSpawn as a promising approach to structured agentic inference, with improved task performance and faster navigation.
量子位
Noam Brown, an OpenAI researcher and core author of o1, discussed his current focus, multi-agent systems, in a recent podcast. He said OpenAI previously used 10,000 agents over 88 hours and 130 billion tokens to solve a Millennium Prize mathematics problem. Brown was explicit that the result came from having a general, powerful model, and that he would not even attribute 10% of the credit to multi-agent methods, which he feels have received disproportionate praise because they are eye-catching. Multi-agent systems simply scale test-time compute in parallel rather than serially, at some efficiency loss since agents do not share full context. His testing reached only 16 agents: four agents solve a problem about twice as fast for roughly double the cost, and 16 still improve performance with slightly lower efficiency. Speedups are somewhat sublinear and domain-dependent, and ablation experiments at 1,000-agent scale would be unaffordable.
