lab Meta AI / FAIR Blog · 3h ago · releases

Meta's SAM 3 and DINOv3 enable real-time scientific image segmentation

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Lawrence Berkeley National Laboratory has deployed Meta’s Segment Anything Model 3 (SAM 3) and DINOv3 within the SYNAPS-I initiative to automate X-ray and neutron data analysis. Fine-tuned on scientific imaging data, these models replace manual segmentation bottlenecks with a pipeline that delivers semantically labeled 3D volumes in approximately 15 minutes.

The system processes tens of petabytes of annual data from DOE light and neutron source facilities.
SYNAPS-I fine-tuned the open-source foundation models on data collected at beamlines like the Advanced Light Source.
Deployed across 300 A100 GPUs at national supercomputing facilities, the pipeline enables real-time interpretation during experiments.
Demonstrated on grapevine drought response, the tool reduces expert annotation time from weeks to minutes.

This approach allows researchers to study dynamic biological processes at the speed of data acquisition while maintaining secure, on-premise infrastructure.

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media r/LocalLLaMA · 32d ago · 4 views

Ohio State University releases open-source Deep Research agent QUEST-35B

Ohio State University's NLP team has released QUEST-35B, an open-source Deep Research agent trained on approximately 32 H100 GPUs using 8,000 synthetic samples. The team open-sourced the training recipe, code, weights, and datasets, with benchmark results showing competitive performance compared to leading closed-source Deep Research systems.

media r/LocalLLaMA · 32d ago

Ohio State University releases open-source Deep Research agent QUEST-35B

Researchers at Ohio State University trained QUEST-35B, a Deep Research agent, using approximately 32 H100 GPUs and 8,000 synthetic samples. They open-sourced the training recipe, code, weights, and datasets, with benchmark results showing competitive performance compared to leading closed-source Deep Research systems.

arxiv arXiv cs.LG · 33d ago

Reverse-Engineering Transformer Attention with Executable Programs

A new method uses program synthesis to generate Python programs that reproduce attention patterns in transformer models. These programs achieve over 75% average Intersection-over-Union similarity on held-out data and can replace up to 25% of attention heads with minimal impact on model performance, increasing perplexity by only 16% on average.

media TLDR AI · 4d ago

Bonsai 27B runs on phones; Kalshi builds compute markets

PrismML has announced Bonsai 27B, the first 27B-class model capable of running on a smartphone. The model achieves this by using 1-bit and ternary weights to reduce its size to 3.9 GB and 5.9 GB respectively.

arxiv arXiv cs.CL · 6d ago

MyAG: A graph-based framework for designing composable LLM agent systems

The authors present MyAG, a graph-based framework designed to facilitate the construction and analysis of composable large language model (LLM) agent systems. The system separates agent architecture into three distinct graph abstractions: a component graph for agents and modules, a workflow graph for execution control, and a search graph for runtime execution.