Notable data and AI papers

627 papers · 45 labs

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM research paper by NVIDIA, 2025

NVIDIA · Architectures · Oct 17, 2025 · 73 citations · 93 upvotes · unverified

Read on arXiv

What it shows

OmniVinci, an open-source omni-modal LLM, enhances cross-modal understanding and performance across audio, vision, and robotics applications with innovative architecture and efficient data curation.

By Hanrong Ye, Chao-Han Huck Yang, Arushi Goel and 29 more · arXiv 2510.15870 · PDF · Code

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 →
Topic
ArchitecturesJun 12, 2017Attention Is All You NeedIntroduced the Transformer, the attention-only architecture behind nearly every large language model since.Google201k citations142 upvotesRead →ArchitecturesDec 1, 2023Mamba: Linear-Time Sequence Modeling with Selective State SpacesA selective state space model that scales linearly with sequence length and matches Transformers on language.Carnegie Mellon University10.2k citations152 upvotesRead →ArchitecturesSep 2, 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and GenerationCodeT5, a unified encoder-decoder Transformer model, improves code understanding and generation by leveraging semantic information from identifiers and user comments.Salesforce2,633 citations3 upvotesRead →ArchitecturesUnverifiedMar 14, 2025VGGT: Visual Geometry Grounded TransformerVGGT, a feed-forward neural network, efficiently infers multiple 3D attributes from single or multiple views, outperforming alternatives and enhancing downstream tasks without post-processing.Meta2,132 citations40 upvotesRead →ArchitecturesUnverifiedJan 11, 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language ModelsThe DeepSeekMoE architecture improves expert specialization in Mixture-of-Experts models by segmenting experts and isolating shared ones, achieving better performance and computational efficiency compared to GShard and other models.DeepSeek1,268 citations65 upvotesRead →ArchitecturesMay 13, 2023CodeT5+: Open Code Large Language Models for Code Understanding and GenerationCodeT5+, a family of flexible encoder-decoder LLMs initialized with frozen LLMs and trained with a mixture of pretraining objectives, achieves state-of-the-art performance across various code-related tasks.Salesforce836 citations7 upvotesRead →
About this paper
Venue
arXiv.org
Citations
73, 6 influential · Semantic Scholar
Upvotes
93 · Hugging Face
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

CitationsNov 2, 2026Citations rose from 65 to 73.
CitationsOct 12, 2026Citations rose from 64 to 65.
CitationsOct 5, 2026Citations rose from 60 to 64.
CitationsSep 28, 2026Citations rose from 58 to 60.
CitationsSep 25, 2026A first count of 6 influential citations.
CitationsSep 25, 2026A first count of 58 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.