MLCommons to Build MLPerf Agents Benchmark Under NSF-Backed AI Efficiency Challenge

MIT and Boston Children's Hospital develop symmetry-aware AI for fetal ultrasound

Researchers at MIT and Boston Children's Hospital developed a model that teaches AI about symmetry to adjust fetal ultrasound scans on the fly, tracking fetal movement during imaging. The approach aims to make prenatal diagnosis more reliable and could spare families repeat hospital visits when scans are inconclusive.

Meta details GenRecs tokenization for generative ads recommendation

Meta published an engineering post on GenRecs Tokenization, the data representation layer behind its Generative Recommenders for ads. Meta says the system shifts from scoring every ad to reasoning over ad content and user preferences using LLM world knowledge, with a stable model vocabulary intended to make ad matching more precise and compound gains for advertisers.

MIT researchers give AI models 3D understanding of crystalline materials

MIT researchers published work aimed at helping AI models understand crystalline materials in three dimensions and reason about their properties. The approach targets a gap in how models represent crystal structures, which are typically described in formats that lose 3D spatial information. The research was announced by MIT News.

Google Research grounds AI agents in wet-lab robotics with VERID framework

Google Research outlined three projects bringing AI agents to physical wet labs, including the VERID framework for safe, resource-efficient experimentation grounded in physical measurements. The team demonstrated closed-loop agentic discovery for aptamer and de-novo protein design, reporting single-digit nanomolar protein binders for cancer targets. The work targets gaps in 3D reasoning and slow experimental feedback.

NVIDIA Details 2.8 Trillion Token Indic Pretraining With Open Data Pipeline

NVIDIA published a Hugging Face blog post on scaling Indic-language pretraining, describing a corpus of 2.8 trillion tokens and an open data generation pipeline. The post covers how the training data was produced and released for building Indic language models.