O que está acontecendo agora

Unlocking asynchronicity in continuous batching

mimalloc: A new, high-performance, scalable memory allocator for the modern era

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy to build and integrate into other projects. It provides bounded worst-case allocation times (up to OS primitives), bounded space overhead, low internal fragmentation, and minimal contention by relying almost exclusively on atomic operations. The post mimalloc: A new, high-performance, scalable memory...

GridSFM: A new, small foundation model for the electric grid

Introducing GridSFM, a small foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings. Learn how GridSFM gives grid operators direct visibility into congestion, stability, and system health. The post GridSFM: A new, small foundation model for the electric grid appeared first on Microsoft Research .

WithinUsAI/GPT_5.5_Distilled

Dataset em destaque no Hugging Face — 976 downloads.

Co-Scientist: A multi-agent AI partner to accelerate research

Introducing Co-Scientist, a collaborative AI partner built with Gemini to help researchers accelerate scientific breakthroughs.

Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models

MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. The post Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models appeared first on Microsoft Research .

Building Blocks for Foundation Model Training and Inference on AWS

BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6

Modelo de modelo · 27 B de parâmetros — 12.2 mil downloads e 133 curtidas no Hugging Face.

SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests

Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize for user interest. The post SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests appeared first on Microsoft Research .

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

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Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs

An axiomatic evaluation framework reveals systematic failures in latent thought representations of LLMs across multiple reasoning tasks, demonstrating that current representations…

nvidia/PhysicalAI-Autonomous-Vehicles

Dataset em destaque no Hugging Face — 208.8 mil downloads. PHYSICAL AI AUTONOMOUS VEHICLES The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, geographically diverse collections of multi-se…