Today’s AI news: editorial picks and emerging stories

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.

The study presents three reproducible probes and quantifies how often ASR models reproduce incorrect benchmark transcripts, with direct implications for evaluation design.

A comparison across eight models shows that the right amount of agent memory varies with model capability and offers two transferable ways to control costs: retrieval and prompt caching.

Hugging Face has published reproduction results and adjudication data for more than 2,200 ICML papers, offering a view of the limits of agents in scientific reproduction.

By separating encoding failures from retrieval failures, knowledge profiling offers a new way to diagnose the sources of factual errors in frontier models.

AMIE extends from text to live video consultations, showing how an asynchronous multi-agent architecture can balance conversational latency with clinical reasoning.

The article reports a verifiability framework and audit metrics, helping readers compare autonomous research systems on citation and code consistency.

Microsoft Research has released 12 computer-use-agent training environments with verifiers and reports comparisons of transfer performance between deep and shallow environments.

The article details the CUDA-to-MLX translation layer and reports two kernel benchmarks, showing how kernel knowledge can be transferred across platforms.