AI for Science

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.

Researchers used AlphaFold to identify how Cas proteins accommodate mismatched DNA targets, then redesigned key amino acids to reduce unwanted editing. The work points to a more targeted route for improving CRISPR safety.

The Trump administration’s first Genesis Mission grants put $5 billion behind AI-driven research. The controversy is less about using AI in science than about remaking public research around political control, private-sector logic, and measurable returns.

SciForma reframes scientific diagram generation around structural fidelity rather than surface-level visual plausibility. It decomposes correctness into components, arrows, and text, then trains models to satisfy all three at once.

A new benchmark, 3D-Fit, evaluates whether general-purpose LLMs can generate 3D ligands under realistic spatial constraints for structure-based drug design. The results suggest early promise, but also expose a clear gap versus specialized diffusion models.

S1-Omni aims to reduce fragmentation in AI for Science by bringing diverse scientific data types into one reasoning model. It supports more than 200 scientific tasks and releases model weights, inference code, and a 10K-sample corpus subset.

A new benchmark shifts plasma diagnostic ML evaluation from clean sensor data to realistic failure conditions. The results show that sequence models can break down sharply when sensors fail near a disruption.

The first three operational FireSat satellites have reached orbit, bringing a purpose-built wildfire detection constellation closer to service. Backed by Google and other funders, the system combines multispectral imaging with AI analysis to detect small fires earlier.

A new arXiv position paper argues that autoformalization should evolve from translating isolated statements into building complete, machine-checkable theory libraries.

The paper introduces Mycelium, an active shared workspace designed to connect researchers and AI agents so that scientific observations and hypotheses can move to the people or systems able to act on them.

SDABench shifts the evaluation of LLMs in scientific data analysis from workflow completion to the capabilities needed to support scientific claims.

A new arXiv paper introduces MOJO, a joint training framework that combines masked autoencoding with supervised decoding for spike-tokenizing neural models. The approach aims to make better use of unlabelled neural recordings, especially when behavioural labels are scarce.

A new arXiv paper tests whether frozen Evo 2 representations contain linearly accessible biosecurity-relevant information. Lightweight probes show strong performance on antimicrobial resistance detection, while virulence and generated-sequence labels remain more limited.

A new arXiv paper examines feature selection in renewable power prediction and proposes Cluster-based Sequential Feature Selection. CSFS matches the predictive performance of standard sequential feature selection while cutting computational cost by 21% on average.

A new arXiv paper extends empirical Bayes variational autoencoders to jointly model tumor growth, time to dropout, and genomic covariates. The work combines neural dynamics with semi-mechanistic pharmacometric structure.

A new arXiv paper introduces PlumeQuant, a diagnostic framework for checking whether methane plume masks, IME, plume length, emission rates and uncertainty estimates are internally consistent. The study shows that published scalar quantities often do not uniquely determine the plume boundary.