Notable data and AI papers

Papers627Labs45

627 papers · 45 labs

Transition Matching Distillation for Fast Video Generation research paper by NVIDIA, 2026

NVIDIA · Multimodal and robotics · Jan 14, 2026 · 30 citations · 34 upvotes · unverified

Read on arXiv

What it shows

Transition Matching Distillation enables efficient video generation by distilling diffusion models into few-step predictors using conditional flows and semantic representation decomposition.

By Weili Nie, Julius Berner, Nanye Ma and 3 more · arXiv 2601.09881 · PDF

UnverifiedHugging Face's summary; not yet checked by hand.

More from NVIDIA

All 90
Multimodal and roboticsUnverifiedNov 5, 2026VFlash: Fast, Accurate Visual Planning via Flash Primitive DecodingA method called VFlash speeds up and improves visual motion planning for robots by predicting entire spatial shapes at once.NVIDIA109 upvotesRead →Foundation modelsUnverifiedOct 27, 2026Nemotron-Labs-Diffusion-Uni: Elastic Self-Refining Unified Diffusion LLM with Cross-Paradigm On-Policy DistillationNLD-Uni is a unified discrete diffusion language model that combines visual understanding and generation via cross-paradigm on-policy distillation and progressive self-refining diffusion.NVIDIA12 upvotesRead →ReasoningUnverifiedOct 27, 2026ThinkWorld: World Imagination Reinforces Chain-of-Dynamics ReasoningThinkWorld improves robotic generalization by representing actions as dynamics chains and using world imagination to learn from unlabeled human videos.NVIDIA57 upvotesRead →Multimodal and roboticsUnverifiedOct 20, 2026Joints, Camera, Action! Co-Generating 3D Motion and Video for Arbitrary RigsThis model generates both video and 3D motion for any character rig, allowing the motion to be replayed accurately within a 3D scene.NVIDIA26 upvotesRead →Inference and efficiencyUnverifiedOct 13, 2026EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud DelegationThis paper introduces a framework that reduces inference costs by using a small local model for most tasks and delegating only critical steps to the cloud.NVIDIA30 upvotesRead →Training and scalingUnverifiedOct 8, 2026When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often BetterThis paper shows that distilling from offline student rollouts is often more efficient and accurate than standard on-policy distillation for many model pairs.NVIDIA28 upvotesRead →Multimodal and roboticsUnverifiedOct 7, 2026Long-WAM: Scaling the Context of World-Action ModelsLong-WAM improves robot control by using autoregressive pretraining to effectively leverage long visual histories for real-time action prediction.NVIDIA2 citations114 upvotesRead →Inference and efficiencyUnverifiedOct 6, 2026NeMo-DCR: Bit-Exact Delta-Compressed Refit for Scalable Agentic RL at Trillion-Parameter ScaleNeMo-DCR enables efficient, bit-exact synchronization of trillion-parameter model checkpoints by transmitting only the sparse weight changes between clusters.NVIDIA1 citations29 upvotesRead →

Other multimodal and robotics papers

Topic
Multimodal and roboticsApr 5, 2023Segment AnythingA promptable model and a dataset of over a billion masks that cut out any object in any image.Meta16.8k citations6 upvotesRead →Multimodal and roboticsJan 30, 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsBLIP-2 efficiently pre-trains vision-language models using off-the-shelf frozen encoders and decoders, achieving state-of-the-art performance with fewer parameters.Salesforce9,805 citations3 upvotesRead →Multimodal and roboticsJan 28, 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationBLIP, a Vision-Language Pre-training framework, improves performance across both understanding and generation tasks by bootstrapping captions from noisy web data, achieving state-of-the-art results on image-text retrieval, image captioning, and VQA, and showing strong generalization to video-language tasks.Salesforce7,655 citations5 upvotesRead →Multimodal and roboticsUnverifiedOct 25, 2024GPT-4o System CardGPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.OpenAI5,388 citations88 upvotesRead →Multimodal and roboticsUnverifiedNov 20, 2025SAM 3: Segment Anything with ConceptsSegment Anything Model 3 achieves state-of-the-art performance in promptable concept segmentation and tracking by leveraging a unified model architecture with decoupled recognition and localization.Meta1,466 citations138 upvotesRead →Multimodal and roboticsUnverifiedMar 8, 2024DeepSeek-VL: Towards Real-World Vision-Language UnderstandingDeepSeek-VL is an open-source vision-language model that achieves state-of-the-art performance in real-world applications by combining a hybrid vision encoder with effective pretraining strategies to preserve language model capabilities.DeepSeek939 citations49 upvotesRead →
About this paper
Venue
Computer Vision and Pattern Recognition
Citations
30, 1 influential · Semantic Scholar
Upvotes
34 · Hugging Face
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

CitationsNov 9, 2026Citations rose from 29 to 30.
CitationsNov 2, 2026Citations rose from 25 to 29.
CitationsOct 12, 2026Citations rose from 24 to 25.
CitationsOct 5, 2026Citations rose from 21 to 24.
CitationsSep 25, 2026A first count of 1 influential citations.
CitationsSep 25, 2026A first count of 21 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.