AI News Today

AI-powered voice and audio tools are becoming increasingly embedded in daily life, from digital assistants to smart speakers and customer service bots. Advances in large audio-language models (LALMs), which can both analyze and generate audio, now make it possible to control devices using voice commands, transcribe meetings automatically, or identify a song playing in the background. These models are also increasingly equipped with the ability to communicate with external services and operate ot

Electronic rings wirelessly connected to an AI system are capable of translating multiple sign languages into text, a new study finds. “I believe this is an important step toward making sign language translation systems more practical, lightweight, and usable in real-world environments,” says Ki Jun Yu , an associate professor of electrical and electronic engineering at Yonsei University in Seoul, Korea. More than 300 different sign languages are used worldwide, and many research projects are de

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim. The research aims to develop robust evaluation methods for long-horizon delegated and […] The post Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability appeared first on Microsoft R

A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.

A hydrated leaf is a healthy leaf. That’s true for the leaves of crop plants in a farmer’s field and for the leaves of trees in an area vulnerable to forest fires. But the traditional techniques to monitor leaf hydration require cutting them from their plants, which is time-consuming and cannot give live measurements. That’s why many researchers are building sensors that measure a plant’s health in real time. Now, researchers in Texas have developed a graphene “tattoo” that can be stuck directly

By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.

This sponsored article is brought to you by Applied Materials . At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as those achieved under the Human Genome Project — required a new operating paradigm: Concentrate the world’s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and silo

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

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.

One of the earliest stated goals for computing in medicine was to aid in clinical reasoning: the decision-making steps required to reach a diagnosis and form a treatment plan. And over the years, researchers have built many clinical decision support systems, which have typically been purpose-built, with painstakingly written rules about symptoms, test thresholds, and medication interactions. As artificial intelligence capabilities develop, clinical reasoning is a natural application. Now, a larg

When I sat down with bell hooks’ personal journals at an archive at Berea College in Kentucky, I expected an intimate peek into her private thoughts, her voice before the editing. What I got instead was frustration. Her handwriting was dense cursive, all loops that looked identical to my eye, and there were years of journals to go through. I found myself photographing pages and feeding them to ChatGPT just to read what she’d written. My tool of choice worked well, and it turns out I’m not the fi

This sponsored article is brought to you by Ampace . As AI workloads grow to gigascale levels, the global data center industry has hit a hidden physical wall. The real bottleneck is no longer just the thermal limit of the chip or the capacity of the cooling system — it is the dynamic resilience of the power chain. Modern AI computing clusters, driven by massive GPU clusters, generate high-frequency, abrupt, and synchronized spikey pulse loads. As rack densities soar beyond 100 kW, these fluctuat

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.

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.

Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large language models (MLLMs). In pursuit of ever more detailed and accurate captions, recent work has increasingly turned to reinforcement learning (RL). However, existing captioning-RL methods and evaluation metrics often emphasize a narrow notion of caption quality, inducing trade-offs across core dimensions of captioning. For

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At Apple, we believe privacy is a fundamental human right. As AI capabilities increase and become more integrated into people’s daily lives, advancing research in privacy-preserving techniques is increasingly important to ensure privacy is protected while users enjoy innovative AI experiences. Apple’s fundamental research has consistently pushed the state-of-the-art in this domain, and earlier this year, we hosted the Workshop on Privacy-Preserving Machine Learning & AI. This two-day event

Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset critical failures in others (e.g., safety or formatting), masking low-performing “bottleneck” rewards vital for reliable multi-objective alignment. We propose Reward-Variance Policy Optimization (RVPO), a risk-sensitive framework that penalizes inter-reward variance during advantage aggregation, sh

We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder architecture that compresses input views into a compact latent representation. This latent representation is then decoded into a set of UV-parameterized 3D Gaussians anchored to a neutral head template. This UV representation decouples the number of 3D Gaussians from the number and resolution of input images, en

We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downstream efficiency; and accessible, requiring minimal input, i.e., an unstructured dynamic point cloud, to construct. Specifically, Velox trains an encoder to compress spatiotemporal color point clouds into a set of dynamic shape tokens. These tokens are supervised using two complementary decoders: a 4D surface decoder, w

One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a perceptual yet practical image codec is yet to be proposed. In this work, we aim to close this gap. We conduct a comprehensive study of the key modeling choices that govern the design of a practical learned image codec, jointly optimized for perceptual quality and runtime — inclu

Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.

Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.

Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.

Data Mining & Modeling

Data Mining & Modeling

How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.

A new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations.

Generative AI

Generative AI

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Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.

Generative AI

General Science

A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.

Built in collaboration with the Gray Lab at Johns Hopkins Whiting School of Engineering, the Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.

Education Innovation

Generative AI

Generative AI

Amazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods.