AI research papers.

Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction applied to the output of the previous turn.

Interactive 3D head generation requires coordinated speaking and listening motion that responds to an evolving conversation.

Many language tasks have no single answer that can be checked automatically.

Mobile manipulation extends robot interaction beyond a fixed kinematic workspace by making the reachable region itself controllable.

Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selections, and parameter values.

Robots that learn from a few demonstrations often require two forms of generalization.

Distillation attacks copy the reasoning capabilities of closed-source large language models, allowing bad actors to replicate state-of-the-art performance at low cost.

Large language models (LLMs) excel at structured, verifiable tasks, but their low-entropy bias can produce homogeneous and predictable outputs, limiting their utility for open-ended scientific ideation.

Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision models.

Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, while maintaining world consistency across viewpoints.

World-space hand motion estimation from egocentric video requires recovering 3D articulated hand geometry while tracking camera egomotion.

Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation.

Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed size further, and so use its parameters more fully; while sparse mixture-of-experts (MoE) models activ…

When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs.

Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong, revise it, observe the result, and diagnose again.

A voice agent can handle almost every call on the words alone and still fail the few its sector's rules were written for.

Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantly impairs cooperation.

Prompt injection against LLM agents becomes much stronger when the injected instruction is wrapped in the model's own chat template.

Large language model post-training generates self-generated rollouts through reinforcement learning and on-policy distillation, yet this experience is often treated as stale once the policy advances.

Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth.

We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports.

Multi-step matching models, including flow and diffusion models, produce high-quality outputs but incur substantial inference costs and may reproduce unwanted components of their training datasets.

Long-running LLM agents compress past interactions into persistent memories that may be reused as premises for later tasks.

Before invoking external tools, an agentic LLM must select among a K-way action space: executing a call, seeking clarification, answering directly, or declining.

As large language models are deployed in increasingly autonomous long-horizon tasks, manually auditing and verifying the actions, artifacts, and outputs of models becomes more difficult.

Reinforcement learning has helped AI agents solve increasingly difficult tasks, but high rewards do not always reflect the work users intended.

State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for long-context performance.

Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer cache entries at a fixed stride.

Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from a single command.

A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program whose execution yields a scene that can be inspected, edited, and que…

An activation-steering coefficient sets intervention strength, but requesting a particular degree of persona expression requires a behavioral scale.

We propose RegLLM, a diagnostic harness for bounded autonomy in regulated agentic workflows.

When generating text, a Transformer produces representations of past tokens at every layer, but each layer can normally use only representations from the same depth.

High-performance GPU kernels are essential to modern machine learning systems, yet automatically generating kernels that are both correct and efficient remains challenging.

Generating responsive listener facial motion is an important task for embodied conversational AI.

World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences.

Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis.

We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis.

An agent can complete a task while exhibiting undesirable behavior during execution.

Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recurring routines.

Moving ML-mediated decision making onto privacy-preserving clients decentralises the economic decision along with the inference.

Visual backbones have evolved from Convolutional Neural Networks (CNNs) with local aggregation to Vision Transformers (ViTs) with global interactions, State-Space Models (SSMs) with input-dependent state transitions, and…

Information-seeking agents increasingly operate over information spaces that are too large to process exhaustively.

Masked diffusion language models (MDMs) admit flexible generation orders, making the unmasking strategy an inference decision.

A central goal in robotics is to enable manipulation across changing tasks and environments.

Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase.

Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents.

In realistic education, a solution is often expressed not only in words but in a drawing--a circuit, a geometric construction, a function plot--and a teacher must grade the drawing as carefully as the text.

Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling.

Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, scale, and decoder capacity can all vary a…