AI research papers.

Modern Transformer design and compression both reduce to allocating capacity under a budget.

Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional support.

High-quality texture generation is essential for creating realistic and production-ready 3D assets.

arXiv:2603.26556v3 Announce Type: replace-cross Abstract: Converting a pretrained Transformer into a more efficient hybrid model through distillation offers a promising approach to reducing inference costs.

arXiv:2609.20779v1 Announce Type: cross Abstract: Safety evaluations for large language models rely on surface-form classifiers that report declining harm scores across model generations.

Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demanding precision and repeatability.

Human video datasets offer an abundant and diverse source of interaction data that can complement expensive real-robot data.

Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and Héllo) as unrelated vocabulary entries, which fragments the embedding space, or discard this variation thr…

Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently.

We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model.

On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student.

Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectiveness through iterative refinement.

A pretrained robot foundation policy may execute most of a long-horizon task yet repeatedly fail at a few critical subtasks.

Large language models can hold knowledge they do not report.

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line.

We need studies on conversational AI (CAI) at scale to understand human behavior and shape CAI design.

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers.

Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omni R2V generation.

APort Vault is a benchmark for payment authorization in tool-using AI agents.

We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and 8B scales.

Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle.

Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years.

Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties.

Telecom fraud scripts evolve rapidly and are often designed to resemble routine service conversations, creating two key requirements for audio-based telecom-fraud evaluation.

Visual signals require compact yet sufficient representations for robust downstream prediction.

Spatial intelligence requires more than describing object locations.

Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs.

Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones.

Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs.

Question answering has advanced rapidly with large language models, but predominantly for high-resource languages, in both text and spoken settings.

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy.

Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions.

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget.

As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback.

On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution.

Text-guided image editing must introduce the requested changes while preserving unrelated source content.

3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the…

Agent trajectories record what an agent does and what happens next.

Video diffusion models repeatedly process long spatiotemporal token sequences during denoising, making attention a major computational bottleneck.

Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in deployed agents.

Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-level supervision from a self-teacher with…

World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction.

Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic systems, leaving the effe…

Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing.

Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence.

Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers.

Induced pluripotent stem cell (iPSC) culture increasingly relies on segmentation foundation models, yet deployment on laboratory CPUs and edge hardware requires compression schemes that are both efficient and auditable.

Large Language Models (LLMs) are increasingly deployed in applications that must weigh clashing moral values, yet even strong models exhibit hidden biases and brittle instruction-following across languages.

OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior.