AI research papers.

Highly compressed video autoencoders offer an effective way to accelerate video diffusion models, as the Diffusion Transformer (DiT) operates on far fewer tokens.

Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action.

Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation.

We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically coherent as a scene.

Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agen…

When audio, video, and text disagree, accuracy alone does not show whether to acquire another source or abstain.

A large language model can only use the text that fits in its context window, and it recomputes its internal key-value (KV) state for a prompt every time the prompt is sent.

Video-language models commonly assume that more temporal observations lead to more reliable reasoning.

Native NVFP4 promises to accelerate reinforcement learning (RL) for large language models (LLMs), but achieving stable learning across separate training and sampling engines remains challenging.

Speculative decoding accelerates large language model inference through parallel verification of tokens proposed by a drafter.

Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios.

Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent away from the user's goal.

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed.

OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub.

Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.

A safe action is not necessarily a viable one.

Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent.

Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and refine the checker through repeated compilation and analysis feedback.

Modern recommendation is shifting from platform-centric personalization toward user-governed personalization, where a personal LLM agent can act on the user's behalf across services.

State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length.

Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay growing histories, duplicate one another's work, or stop experimenting…

Tool-augmented speech assistants typically serialize automatic speech recognition, large language model inference, and external tool execution.

Speculative decoding accelerates autoregressive generation in large language models.

As human--AI interactions become more conversational, full-duplex speech language models capable of natural real-time dialogue are growing in importance.

Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a single forward pass.

Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand.

Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation, which motivates low-precision rollout for efficient RL training.

A central capability of embodied agents is to accomplish complex objectives through sequences of interdependent tasks.

Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks.

A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence.

On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger teacher on its own rollouts.

Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt to stateful environments.

As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment.

Scaling robotic foundation models requires diverse training data and reliable evaluation environments.

Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense, fixed-size patch tokens.

LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conventions on their own.

Reinforcement learning with verifiable rewards (RLVR) turns agent experience into learning signals primarily through scalar outcome rewards after interaction.

Sensors are useful not only for understanding the world but also for deciding what to do next.

Agentic reinforcement learning (RL) disaggregates training from rollout, so each policy update must reach the rollout clusters before the next batch.

Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning, context, and tool use is typically engineered.

On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback.

Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus.

Recent game design agents have made substantial progress in generating playable games.

Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, user preferences, documents, and interaction experience.

Assistants from 2011 answered and waited, and agents from 2023 did a task and stopped.

Recent advances in world models have enabled increasingly realistic visual synthesis.

Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify.

Interactive video world models need to generate each video chunk efficiently while responding faithfully to user controls.

Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it.

Current video editors can insert objects but often struggle to make them participate in interactions such as being picked up or manipulated.