University of Cambridge

Headlines · 3

1. 4d
   Scinetics proposes Chain of Science Tokens and COS enzyme design

   Scinetics proposed a Chain of Science Tokens approach that represents sequences, structures, images, omics and measurement data as scientific units a model can read and write alongside language, and applied it in Chain of Structures (COS), which frames enzyme design as four rounds of progressively formed protein structures. The work was completed by researchers from MIT, Harvard, Cambridge, HKUST and Scinetics, with Zhang Zaixi as corresponding author, and code and model resources were shared on GitHub and Hugging Face. The team self-reports first place on five of six enzyme-design benchmark metrics, with EC3 of 57.0% versus 55.3% for CodeFP, pLDDT of 79.3 and TM-score of 0.729; ablations drop EC3 to 53.4% without the reasoning chain and 51.8% without the continuous interface. The accompanying Perspective proposes ScienceRSI, a cross-generation self-improvement loop, and a 100B–300B parameter exploratory window, while noting that real enzyme activity, selectivity and stability still need wet-lab validation.

   36氪 人工智能 · 🔥 0
2. 6d
   Apple publishes tri-modal masked diffusion model study

   Apple's machine learning research team presented a NeurIPS paper introducing what it describes as the first tri-modal Masked Diffusion Models (MDM) pretrained from scratch on text, image-text and audio-text data. The work systematically studies multimodal scaling laws, modality mixing ratios, noise schedules and batch-size effects, and proposes an SDE-based reparameterization that decouples the physical batch size chosen for compute reasons from the logical batch size used to balance gradient variance. The authors also pretrain a preliminary 3B-parameter model on 6.4T tokens that shows strong results in text generation, text-to-image and text-to-speech tasks, describing the effort as the largest-scale systematic open study of multimodal discrete diffusion models to date.

   Apple Machine Learning Research · 🔥 0
3. Oct 8
   OpenAI's math proof release falls short of new field standards

   OpenAI this week published hundreds of claimed solutions to hard math problems (719 manuscripts, per the article), saying it consulted an advisory group of elite mathematicians to avoid the controversy its earlier result sparked. But only ten of the manuscripts included the model's chain of thought, and by the article's account roughly 42% of the proofs had not gone through formalization in Lean; the Advisory Group on Mathematics and Artificial Intelligence (AGMAI), hosted by Princeton's Institute for Advanced Study, said it is ultimately up to the mathematical community to judge whether its recommendations were followed — its first request being to stop testing advanced problems on proprietary models. A paper from Cambridge and King's College London mathematicians also documents at least two discrepancies between OpenAI's natural-language proof and the Lean code for a problem derived from the Navier-Stokes equations, concluding such autoformalized proofs should not be trusted without peer review.

   TechCrunch AI · 🔥 0

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