AI research papers.

Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables that augment the backbone with cheap pa…

BraTS-GoAT evaluates tumor segmentation across heterogeneous populations.

Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases.

Diffusion Transformers deliver state-of-the-art video generation, but their long spatiotemporal sequences make attention the dominant deployment cost, and a deployable low-bit kernel must be accurate and fast.

Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations.

Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs).

We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities?

Computer-use agents are increasingly evaluated in realistic desktop environments, but existing benchmarks provide limited coverage of professional engineering workflows whose outputs are persistent, structured artifacts.

Masked diffusion language models (dLLMs) generate text by iteratively denoising masked tokens with bidirectional attention.

Training a Mixture-of-Experts (MoE) model at long context or large batch size fails as soon as any one component's peak allocation exceeds device memory, so the target is every peak at once, not the average footprint.

Recently, linear attention layers have been increasingly adopted to replace softmax attention at scale for long-context modeling.

Unified multimodal large language models (MLLMs) and multi-agent systems have advanced visual generation.

Natural interaction in digital and physical environments requires continuous perception and timely responses.

Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models…

Concurrent local LLM serving on unified-memory desktops must preserve memory headroom and output fidelity, which speed-only rankings overlook.

Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left the model and become an input.

Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability to reliably shift conversational behavi…

When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect?

Large language models learn similar behaviours, yet it remains unclear what structure they share or how to change one behaviour without disturbing others.

We’re sharing an AI-generated solution to the Navier–Stokes Millennium Prize Problem, including a writeup and a formal proof in Lean.

Can one language model hand its live memory to another without the receiver rereading the context?

Inside OpenAI, coding agents are reshaping AI research.

Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions.

Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexplored.

Autonomous agent systems increasingly depend on reusable skill abstractions for consolidating experiential knowledge and domain expertise.

Multimodal Large Language Models (MLLMs) often struggle with hallucinations, thus hindering their reliable practical applications.

Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience.

We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, and Odia.

Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science.

Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time.

Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provide limited safety guarantees.

What if pathology foundation models could do more with less?

Modern AI agents routinely cross trust boundaries: they ingest untrusted content, combine it with privileged instructions, persist intermediate beliefs in long-term memory, and invoke privileged tools.

Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to trac…