All developments.

Sparse attention is a primary approach to reducing the latency of diffusion transformers in long-sequence generation tasks, such as video and high-resolution 3D asset generation.

Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce.

As agents take on long-horizon tasks, users shift from making individual decisions to overseeing autonomous execution.

Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step TildeΔ a learned function of the input.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in video understanding, yet their reliance on retrospective summarization and text-centric priors often limits their ability to bridge unobs…

Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows continuously with the generated history.

Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switching and inconsistent continuation across action chunks.

Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles.

Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural generators, continue to violate physical laws and simple symbolic rul…

Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structures remains underexplored.

Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation.

Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent.

Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills.

Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies.

Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment.

Partnered human-humanoid interaction couples locomotion with continuous physical contact.

Evaluation budgets in agentic retrieval-augmented generation span questions, search trajectories, and repeated answers.

Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in novel view synthesis, yet deploying both static and dynamic Gaussian representations on resource-constrained mobile devices remains…

Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, making multi-view generation expensive at inference time.

Self-evolving reasoning models learn from their own generated questions, yet repeated self-training can lead to performance collapse.

Vision-language-action (VLA) models face a timing gap between low-rate inference and high-rate robot execution.

World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observation reconstruction or generic latent prediction and lack structured,…

On-policy distillation (OPD) has emerged as a widely used paradigm for post-training large language models, reducing the train--test mismatch of conventional distillation by supervising the student on its own generated t…

Recently proposed Drifting Models shift iterative distribution refinement from inference to training, enabling effective one-step generation.

Anthropic is investing $100 million in Claude Frontier Academy to train 10,000 Frontier Deployed Engineers by the end of 2027, with cohorts from Accenture, Bain, CBA, Deloitte, McKinsey, Morgan Stanley and Novo Nordisk a…

Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production.

Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4.

Reinforcement learning for long-horizon agents relies on purely retrospective training signals: credit is assigned only after observing environmental consequences, leaving the agent's belief at action time invisible to t…

Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with latency dominated by sequential decoding.

A benchmark score compares a system output against a reference, and methodological attention falls almost entirely on the first term.

We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes.

Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal captures the full range of human preference.

Recent text-to-image models have made substantial progress in photorealism, aesthetics, and text-image alignment.

We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stores a new pair of key-value representations for every data point duri…

Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs high computational costs that render real-world deployment increasingly…

Language-model systems can separate contextual computation, persistent storage, and exact execution instead of updating all capabilities through one shared parameter system.

On-policy distillation (OPD) has become an important approach to language model post-training.

Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying multilingual toke…

Many useful language-model tasks cannot be evaluated by exact outcome verification.

A trainable input embedding table assigns each vocabulary item an independently adjustable vector.

Language model agents are deployed with a harness, the software around the model that manages its context, tools, and feedback.

Advanced AI may matter most for the routine work behind breakthrough ideas.

Barclays, the British universal bank, is expanding its strategic collaboration with Anthropic to integrate secure, enterprise-grade AI systems across its global operations.

As it opens a new location, the social club prepares grant applications in 2 hours instead of 3 days and liquor-license materials in 3 hours instead of 4 days.

Biological reasoning models use post-training to connect LLMs to biological foundation model representations and biological text.

Multi-agent systems (MAS) built from large language models coordinate specialized agents to tackle complex tasks, but effective workflows are difficult to design in advance.

Recovering executable CAD programs from 3D meshes is challenging due to the compositional nature of CAD construction and the interaction between discrete modeling choices and continuous parameters.

Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordinated subtasks.

Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics.