3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it.
In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching.
Distribution Matching Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it must keep an auxiliary diffusion model fitted to the student's evolving…
Enterprises adopting retrieval-augmented generation (RAG) face a recurring operational decision: promote, revise, or block a system version.
Extreme space-weather events can damage power systems on Earth and degrade GPS accuracy and satellite operations.
Proof of concept for watermarking AI-generated proteins while preserving biological function.
OpenAI is partnering with America’s SBDC to expand hands-on AI training and local support for small businesses, alongside a new report on how small teams are using AI.
Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists.
Diffusion models with prompt and reference image-guided editing have seen rapid progress, yet they remain too coarse for pixel-level control.
Large Language Models (LLMs) have been used for novel discovery of algorithms, theorems, drugs, and other tasks through the use of harnesses that prompt an LLM to iteratively optimize an objective.
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens.
Precise grounding matters.
Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates salient factors specific to the target from common content shared by both.
Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with context length and batch size.
Foundation models can serve as clinical agents through tool-use harnesses.
Recent joint video-audio generation models have achieved strong semantic correspondence and temporal synchronization.
Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation.
Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images.
Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions remain sensitive to task-irrelevant prompt features.
Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI).
Modern transformers pair impressive capabilities with substantial memory and compute demands.
Repeated training on model-generated data can degrade later models.
Biology doesn't operate in silos, and neither should the AI representation of it.
Coding agents can now write, run, and debug programs with little human help.
Explore more than 20 announcements from OpenAI DevDay 2026, including GPT-6 Astra, ChatGPT, Codex, APIs, security, and new tools for builders.
Dots by OpenAI are proactive assistants that can keep working across complex projects and everyday tasks.
On-policy distillation (OPD) trains students using teacher feedback on their own sampled responses, yet how prompt choice shapes transfer across teacher-student pairs remains poorly understood.
Historical street-view imagery records urban evolution, but uneven coverage leaves substantial gaps in historical records.
Language-model agents are usually trained by reinforcement learning from one reward per episode, and privileged self-distillation enriches it by letting the same policy, given a skill, teach its skill-free self through t…
Evaluating the physical consistency of generated videos remains a fundamental challenge.
Process reward models (PRMs) have become a key component for LLMs, as their step-level feedback supports both post-training and test-time reasoning.
Video understanding agents acquire evidence through an executable harness that controls what they observe and how they use those observations.
Optical Character Recognition (OCR) is evolving from plain-text transcription toward general visual intelligence, requiring models to recognize, localize, and reason over textual information in complex visual environment…
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations.
World models, game simulators, and long-take video creation require coherent scene evolution and sustained dynamics over extended durations.
Few-step streaming audio--video generation requires both causal modeling and step distillation, yet standard training recipes face two context-related challenges.
Text-conditioned full-body human-object interaction (HOI) generation requires synthesizing human motion and object trajectories that match the input text while remaining precisely coordinated over time.
Linear attention replaces growing KV caches with fixed-size recurrent states, yet these persistent states can become a substantial memory bottleneck under concurrent serving.
Since launching a year ago, the Microsoft Research Asia — Singapore lab has established a strong foundation, deepened collaboration across government, academia, and industry, and explored how frontier AI research can cre…
OpenAI apologises for incidents involving Australian government websites and outlines stronger safeguards and support to strengthen Australia’s cyber defences.
Our early guidelines for safety cases in frontier AI training cover technical safeguards, operational practices, and investigating misalignment incidents
Mistral opens a Munich hub for Physics AI and Industrial AI research, partnering with German industry.
OpenAI is expanding the Lenfest AI Collaborative and Fellowship Program with $5 million in funding and up to $5 million in software credits and engineering support.
GPT-6 Astra completed a 50-tab tax workbook twice as fast as GPT-5.6 Sol, and its stronger understanding of user intent gives Basis more confidence in real-world use.
Modern AI models are aligned through post-training to adapt them to downstream tasks.
Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the current policy and the data-generating policy.