AI for Science

BaguanHR takes a data-first approach to 0.1-degree global forecasting by using variable-wise super-resolution to expand coarse reanalysis data. Its results suggest that scaling the training set can deliver measurable forecasting gains.

Anthropic has introduced a research preview of the Model Hardware Standard, an interface designed to connect AI agents with laboratory equipment, robots, and other physical systems. The proposal focuses first on scientific workflows, where integrating hardware from different vendors can be especially time-consuming.

A new environment called Station lets agents from different model families choose research directions, run experiments, and build a shared mathematical literature without a scripted pipeline. The paper reports new results on several construction problems and releases dialogues, proofs, and verification code.

RIBOSPAN is a 1.61-billion-parameter bidirectional RNA foundation model pretrained with contexts of up to 10,240 nucleotides. Its evaluations suggest that native long-context training is more reliable than simply extending a short-context model at inference time.

AutoResearch links research ideation with experimental execution in a two-stage autonomous research system. Its design uses cross-model review, iterative diagnosis, and independent evidence checks to reduce unreliable findings.

London startup Inherent says its Faraday agent outperformed larger Anthropic and OpenAI systems on independently reproducing published research. The more important story may be its attempt to train scientific judgment, not simply generate correct answers.

A new study introduces Top-K prompting and training for single-step retrosynthesis, allowing language models to produce multiple chemically plausible proposals. Its C3LM model, trained on roughly 45.6 million verified reactions, shows strong performance on the OOD URSA-expert-2026 benchmark and complements conventional systems.

OmniScientist is an end-to-end AI scientist designed to reason directly over heterogeneous raw evidence rather than only text, labels, or precomputed features. Its pipeline links perception with ideation, experimentation, and scientific writing.

MathForm combines Mathlib retrieval, compiler diagnostics, and semantic-consistency feedback to refine mathematical formalizations iteratively. Its 8B model is trained on roughly 367K verified Lean 4 examples and outperforms several specialized 32B systems on the reported benchmarks.

Personalized Auto-Research argues that a genuine AI co-scientist must understand a researcher’s prior work, methods, collaborators, and communities instead of producing a generic plan for a shared goal.

A new FAR pipeline lets experts specify a research direction rather than a single problem, then uses literature search, model attempts, and automated triage to prioritize mathematical work. A combinatorics pilot illustrates how this design can concentrate scarce expert attention on the most promising candidates.

Large Discovery Model combines a generative model with a Bayesian non-parametric reward surrogate, allowing AI systems to propose candidates while using experimental feedback and uncertainty to guide the next search.

Researchers from DeepMind and collaborators have redesigned the optimization problem behind combination loss analysis and refined the search with machine learning and AlphaEvolve. The work improves the known upper bound on the matrix multiplication exponent from 2.371339 to 2.371177.

CADENA reconstructs a 3D mesh as an editable parametric CAD program by adding operations one at a time and checking the intermediate geometry. The work also introduces CADENA-Bench, a benchmark focused on real mechanical parts.

EMBL AI Librarian reframes Europe PMC as a natural-language evidence interface for AI agents. Instead of returning only paper lists, it plans searches, reads selected papers, and surfaces snippets that support an answer.

Anthropic reportedly used an unreleased Claude Mythos model for cryptanalysis, producing results involving HAWK and reduced-round AES. The episode is less about AI “breaking cryptography” and more about how AI may reshape expert research workflows.

DeepMind’s former AlphaFold team is no longer operating as a standalone unit, according to the report. Its members have moved into Gemini, AI coding, genomics, protein and enzyme design, fusion research, Isomorphic Labs, and in some cases Anthropic.

Anthropic says its Mythos security model helped uncover a serious weakness in HAWK, a post-quantum signature candidate under NIST review. The result does not break deployed cryptography, but it signals how AI could accelerate cryptographic research.

OpenAI says it will give 100,000 academic researchers free access to its most advanced ChatGPT models. The move highlights how frontier AI tools are being positioned as infrastructure for scientific work.

Anthropic says Claude Mythos Preview helped improve an attack on the post-quantum signature candidate HAWK and found a faster attack path against a reduced-round version of AES. The results do not affect production systems today, but they point to a new frontier for AI-assisted cryptographic research.

IDEAgent tackles a core weakness in LLM-based research ideation: systems often optimize either quality or diversity, but not both together. The paper introduces a multi-agent framework and a Yield metric to measure diverse, quality-qualified idea sets.