Today’s AI news: editorial picks and emerging stories

DeepMind’s use of peer review provides a concrete comparison with OpenAI’s publication of AI proofs on GitHub amid a debate over how AI mathematics should be evaluated.

The piece traces how two OpenAI manuscripts extend Wang and Zahl's three-dimensional Kakeya work to a stronger maximal-function version and to four dimensions, while noting the proofs lack peer review.

A prospective clinic study of a patient-facing conversational diagnostic system, with clinician-reported preparation and diagnosis-agreement figures from real urgent care visits.

The three-month patent-writing experiment separates AI's immediate productivity gains from changes in unaided judgment, providing comparative evidence on AI's effect on professional skills.

The source gives a complete recipe for fine-tuning Nemotron 3 to gold-medal-level results at IOI and IMO, which readers can use to reproduce its data and reasoning workflow.

OpenAI made results by an internal frontier model on open math problems public alongside Lean formalizations, showing how model reasoning can be combined with formal verification.

The agent benchmark from Microsoft and Hugging Face uses 507 stateful workflows and 20 repeated runs, shifting the focus from tool calls to the final database state.

Meta made six papers from mathematicians' collaboration with Muse Spark public and described the division of work between people and AI and the review process.

Scores for the same model differ by nearly half across agent harnesses. This open guide gives a multi-harness RL training path without changing the harness, along with measured comparisons.

Google described a new TEE-based federated learning system and its deployment in Gboard, showing an engineering path for privacy-preserving on-device training.

SynthID has expanded from image watermarking to protein design, with a paper and open-source tools released together, offering a technical path for biosecurity governance.

Google breaks long-video generation into four composable frameworks and reports continuity improvements across several benchmarks, illustrating how multi-agent orchestration can reduce semantic drift.

Microsoft Research measured robot inference workloads and reports performance and battery-life data for offloading inference from onboard hardware to edge or cloud GPUs.

Google Research lets teachers generate interactive learning simulations from course objectives and shares teacher-evaluation feedback and a pilot access point.

The article gives the scale of predicted effects for 900 million single-nucleotide variants and free academic access points, helping readers assess the availability of genome-interpretation resources.

Google, HHMI Janelia, and partners have released the largest brain map by neuron count, showing a concrete application of AI in connectomics.

The Open ASR Leaderboard has added its first Hindi and Indian English benchmarks, showing how speaker attributes are being incorporated into public evaluations.

The article reports quantified improvements for PPE in public health, food security, and epidemic forecasting, giving a view of the practical limits of geospatial modeling automation.

The paper presents a repair recipe that distills from the original model after compression and quantization, with stability comparisons against QAT.

T-Rex offers a transferable approach to contact-rich robot learning by combining human video pretraining, synchronized tactile robot data, and separate visual and tactile processing rates.