AI research papers.

Scaling laws suggest that increasing model size, training data, and compute generally yields monotonic performance improvements.

In joint-embedding predictive architectures (JEPA), an encoder and a predictor are trained together to produce a representation that minimizes the error in predicting the representation of the next observation.

LLM agents can improve across tasks without updating model parameters by storing experience from their own executions in external memory and reusing it to guide later decisions.

Agents need reliable ways to validate their outputs, both when completing tasks and when filtering synthetic training data.

Despite substantial progress in Deep Search agents, their ability to conduct end-to-end research in real-world open Web environments remains insufficiently evaluated.

Generalizable robot manipulation requires predicting how a scene will evolve, identifying where interactions are feasible, and determining how to act.

Neural simulation of high-fidelity deformable bodies is a foundational challenge in computer graphics and physical AI.

We present Épi, an autonomous research agent that sustains investigations across model sessions and evolves its methods through accumulated experience.

AI coworkers must coordinate tools, communicate across turns, and act on changing workspace state while respecting workflow constraints.

Feed-forward 3D Gaussian Splatting (3DGS) promises instant scene reconstruction with high-quality novel-view synthesis.

Flow-matching vision-language-action (VLA) models generate an action chunk by iteratively denoising Gaussian noise, but typically execute only a short prefix before replanning with updated observations.

A guardrail classifier is reported as if unsafe were a property of text.

Model--harness co-evolution has emerged as a prominent paradigm for recursive self-improving (RSI) agents, jointly evolving their execution scaffolds and intrinsic capabilities.

Video Reasoning Segmentation (VRS) requires a model to identify an implicitly described target and predict its pixel-level mask trajectory, while resolving the spatial relations and temporal events that determine the ans…

Large language models need effective ways to acquire and use external knowledge beyond what they retain from pretraining.

Biometric authentication systems increasingly use privacy-preserving mechanisms to protect sensitive templates.

People ask conversational AI models about their legal rights, obligations, procedures, and personal situations that may have legal consequences.

Speech anonymization has focused on the acoustic signal, leaving open how much speaker information the transcribed words themselves retain.

Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible.

Estimating a humanoid robot's internal state from video has a wide range of real-world applications but remains unexplored.

Emerging Joint-Embedding Predictive Architectures (JEPAs) are moving beyond representation learning towards latent world models that support prediction, planning, and control.

Digital art generation has undergone a massive paradigm shift, moving from static image synthesis to dynamic video generation that can mimic the human painting process.

Large-scale action pretraining equips Embodied Foundation Models (EFMs) with strong manipulation capabilities, with particular strengths in generalization and closed-loop robustness, but reproducing such pretraining requ…

Multi-turn image editing aims to modify an image according to a sequence of instructions, enabling more effective interactive visual content creation for users.

Bimanual manipulation with dexterous hands has attracted growing attention in research on robotic action models.

Test-time scaling (TTS) improves large language model (LLM) reasoning by sampling multiple answers, but fixed sampling budgets ignore both query difficulty and the changing energy cost of inference under GPU contention.

Coding agents keep working on runs that will fail verification, and asking a frontier model to watch for this is expensive in its own right.

Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks.

While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and inference latency, limiting their practical…

Improving every agent in an LLM-based multi-agent system can be prohibitively costly, making it important to identify which agent should be prioritized to achieve the greatest system-level gain with limited resources.

Effective test-time personalization requires LLM agents to adapt to query-dependent user preferences from lightweight interaction.

Recent advances in general-purpose AI models and agentic systems have enabled them to perform some CAD tasks.

We introduce avatar referring segmentation, which selects regions of animatable Gaussian avatars from language queries.

Recent research-level physics-reasoning benchmarks for LLMs reveal a failure mode that lies not in knowledge or execution, but in modeling the world being reasoned about.

Evaluating software agents on a full benchmark is expensive.

We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware.

Recent works show that LLMs can generate kernels with strong performance on server GPUs.

Agent scaffolds increasingly delegate small decisions (whether to stop, verify, or change approach) to cheaper models.

High-quality image editing requires precise modification of target regions while preserving the content of unrelated regions.

Existing Multi-token prediction (MTP) methods are auxiliary by design: the multi-horizon loss mostly trains modules attached to the backbone, and each added horizon costs another module and another pass.

Unified audio-video creation requires a single model capable of text-conditioned joint synthesis, bidirectional cross-modal generation, and instruction-guided editing across both modalities.

While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables or unstructured data, real-world analytical questions frequently span heterogen…

Safety in Multimodal Large Language Models (MLLMs) depends on unsafe multimodal data for post-training alignment or activation steering.

Recursive self-improvement (RSI) relies on evaluation feedback to assess progress and guide further research, yet repeatedly running complex benchmarks is costly and slows iteration.

The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world.

Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input.

Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that mediates their interaction with the environment.

Recent advances in large language models (LLMs) have brought impressive performance across diverse tasks; however, shortcomings remain in logical reasoning, including inconsistencies under linguistic variation.

Vision-language-action (VLA) models have shown strong promise for generalist robotic manipulation, but their inference cost remains a major bottleneck for real-time control.

Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions.