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Teleoperation remains the default way to collect robot demonstrations, but every episode costs robot and operator time, and every new task starts from scratch.
Looped transformers are a parameter-efficient alternative to standard transformers for language modeling.
Continual learning in physical AI differs from conventional continual learning because small per-step policy errors can accumulate over a trajectory.
Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient…
Language models are accurate on most long-arithmetic problems, but their accuracy is estimated on samples and carries no guarantee for an unseen input; tool…
A frontier AI model is trained in an AI data center, on tens of thousands of accelerators joined by a dedicated network, and every training step ends with all…
Video generation has progressed from isolated clips to coherent short-form audiovisual sequences, and the bottleneck is shifting from rendering to semantic…
Among 291 surveyed open-weight language model repositories (280 distinct embedding tables), 52.6% of untied gated and hybrid checkpoints (20/38) contain…
A robot world model that serves as a simulator must do more than generate plausible videos: it must faithfully follow commanded actions, maintain consistency…
Unified Multimodal Models (UMMs) integrate visual understanding, generation, and editing within a single unified framework.
Artificial general intelligence (AGI) requires a method that can understand, generate, reason, and act across diverse domains and modalities.
Unified multimodal models (UMMs) integrate image generation and visual understanding, but are existing single-pathway unlearning methods sufficient to suppress…
In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context.
Large language models are deployed as long-horizon agents that compose diverse tools across MCP services into executable workflows.
Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet…
The advancement of multimodal learning is heavily constrained by the scarcity and high annotation cost of high-quality instruction data.
Byte level language models avoid tokenizer dependence and provide universal coverage, but processing every byte at full resolution is expensive.
Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations.
Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text.
Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing…
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech despite the abundance of web audio.
Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance.
Speculative decoding has been treated purely as a systems optimization that speeds up large language model decoding without changing the model behavior.
Missing modality imputation aims to synthesize unobserved modalities from observed ones.
Why do language models fail on long inputs? The common answer is length. We show that, for retrieval, length is cheap and competition is not.
Reasoning vision-language-action (VLA) models pair physical control with natural-language rationales that supervisors can read to monitor embodied safety.
A speech deepfake detector estimates whether a recording is synthetic, but a forensic analyst also asks which system produced it, whether recordings share a…
Pretrained Vision-Language-Action (VLA) policies provide a strong starting point for deployment, but adapting them to a target robot and environment still…
Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) to consolidate capabilities from multiple teachers.
Multimodal large language models (MLLMs) can now process hour-long videos, making extended multi-turn dialogue over a single video a natural mode of use.
Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers.
Multimodal agents use web and image tools to answer questions beyond their internal knowledge.
Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair different failures, and their utility changes as…
A deployable text-to-image diffusion model must hold several capabilities at once, such as instruction following, text rendering and human preference, and must…
Industrial recommender systems allocate substantial computation to pointwise candidate scoring, yet separate reranking and mix-ranking stages still assemble…