全部 AI 动态

Introducing Gemini 3.7 Flash

What We Learned by Reproducing 2,200 papers from ICML

Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

MindTopo reveals VLMs’ spatial reasoning abilities

A path, a fence, a knot. MindTopo sets a new benchmark for testing how AI understands topological relationships and highlights new opportunities to strengthen spatial reasoning and planning. The post MindTopo reveals VLMs’ spatial reasoning abilities appeared first on Microsoft Research .

Empty shelves or lost keys? Recall is the bottleneck for parametric factuality

Generative AI

Advancing AMIE towards expert-level audio-visual clinical consultations

Health & Bioscience

Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research .

Thinking of ACE? We Can Do It with Fewer Tokens

With a feel for physics, AI models simulate a wider range of real-world scenarios

“GeoPT” helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately.

Release: v5.15.0

Release v5.15.0 New Model additions Meta Muse Glimmer Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups. Muse Glimmer is a dense 30B parameter model consisting of: 2B ViT-style encoder for vision (Perception Encoder) 28B parameter text decoder We're covering it in the following blogpost: http://hf.co/blog/muse-glimmer GraniteMoeSWA & GraniteSWA Links: Documenta

Making Knowledge Distillation Cheap Enough to Run at Scale

Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

WeatherNext: AI model achieves breakthrough in forecasting cyclones

Solving the solvent problem

By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.

The benefits of medical AI assistance vary based on user expertise

Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.

Alexander Rakhlin named director of the MIT Statistics and Data Science Center

An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra.

Daniela Rus receives Bavarian Minister-President's High-Tech Prize

Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.

EvoLib: Turning experience into evolving knowledge

LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research .

How a medical database developed at MIT evolved into a global standard of data-sharing

The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.

Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83

Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.

Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.

Controlling Reasoning Effort in LLMs

How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes

Following the questions where they lead

Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive.

Introducing Gemini 3.5 Flash Cyber

Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.

A better way to turn 2D designs into 3D models for rapid prototyping

Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.

3 Questions: Neural transparency and the future of AI design

Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.

Release v5.14.0

Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI- powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-foll

Towards demystifying the creativity of diffusion models

Algorithms & Theory

SensorFM: Towards a general intelligence and interface for wearable health data

Generative AI

Release v5.13.0

Release v5.13.0 New Model additions KimiK 2.5, 2.6, and 2.7 This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7: Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration. The model was proposed in Kimi K2.5: Visual Agentic Intelligence and further improved in [Kimi K2.6: Advancing Open-Source Coding](Kimi K2.5: Visual Agentic Intelligence). Kimi K2.5 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming langua