Skip to content
Papers.

Sol-Attn: Accelerating Video Generation Inference via On-the-Fly Attention Sparsification research paper by NVIDIA, 2026

NVIDIA · Jul 27, 2026 · Inference and efficiency · 2 citations · 37 upvotes · unverified

Read on arXiv

What it shows

Sol-Attn improves training-free sparse attention for diffusion transformers by combining dynamic block routing, sparse computation, and approximation correction in a single online pass to accelerate video generation without sacrificing quality.

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

More from NVIDIA

All 75
PaperCitations
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent HarnessAs coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback.Agents and evaluation · Sep 2026 · Unverified1
An Open Recipe for IMO Gold: Training Nemotron for Olympiad MathematicsA natural-language proof-generation pipeline using post-trained Nemotron 3 Ultra checkpoints achieves gold-medal performance on IMO 2026 through iterative verification and refinement without external tools.Training and scaling · Sep 2026 · Unverified0
Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-TrainingA system for large-scale online draft co-training accelerates speculative decoding in RL post-training by extending context-parallel attention and adding cross-stage feature transport.Training and scaling · Sep 2026 · Unverified1
SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video GenerationSANA-Video 2.0 is a hybrid video diffusion transformer that combines linear and softmax attention to generate high-resolution video efficiently on a single GPU.Inference and efficiency · Jul 2026 · Unverified2
Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement LearningMolt is a compact PyTorch framework for agentic reinforcement learning that enables efficient asynchronous training of multimodal and mixture-of-experts policies with minimal overhead.Agents and evaluation · Jul 2026 · Unverified1
NVIDIA-labs OO Agents: Native Python Object-Oriented AgentsNOOA treats AI agents as Python objects whose methods and fields define actions, state, and prompts, enabling deterministic testing and LLM-driven runtime completion within a unified programming model.Agents and evaluation · Jul 2026 · Unverified0
ASPIRE: Agentic /Skills Discovery for RoboticsASPIRE is a continual learning system that autonomously develops and refines robot control programs through iterative exploration, achieving superior performance and zero-shot generalization in manipulation and household tasks while enabling sim-to-real transfer.Agents and evaluation · Jun 2026 · Unverified21
PhysisForcing: Physics Reinforced World Simulator for Robotic ManipulationPhysisForcing enhances embodied video generation by enforcing physical consistency through pixel-level trajectory alignment and semantic-level relational alignment losses in a DiT-based framework.Multimodal and robotics · Jun 2026 · Unverified3

Other inference and efficiency papers

Topic
PaperCitations
Efficient Memory Management for Large Language Model Serving with PagedAttentionvLLM manages the KV cache like pages of virtual memory, serving models with 2 to 4 times the throughput.UC Berkeley · Sep 20238,481
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessFlashAttention computes exact attention with far fewer GPU memory reads and writes, making long sequences faster.Stanford University · May 20225,334
Group Sequence Policy OptimizationGroup Sequence Policy Optimization (GSPO) is a reinforcement learning algorithm that improves training efficiency and performance of large language models by using sequence-level importance ratios and operations.Alibaba (Qwen) · Jul 2025 · Unverified688
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse AttentionNSA, a trainable sparse attention mechanism, enhances long-context modeling efficiency without sacrificing performance, achieving improvements in speed and accuracy over full attention models.DeepSeek · Feb 2025 · Unverified507
SmolVLM: Redefining small and efficient multimodal modelsSmolVLM, a series of compact multimodal models, achieves high performance with minimal GPU memory usage, making efficient deployment on mobile and edge devices possible.Hugging Face · Apr 2025 · Unverified294
Inference-Time Scaling for Generalist Reward ModelingSelf-Principled Critique Tuning enhances pointwise generative reward modeling for large language models, improving scalability and quality compared to existing methods.DeepSeek · Apr 2025 · Unverified249
About this paper
Authors
Haopeng Li, Yitong Li, Junsong Chen and 8 more
arXiv
2607.24027 · PDF
Venue
arXiv.org
Citations
2, 0 influential · Semantic Scholar
Upvotes
37 · Hugging Face
Lab
NVIDIA · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 2Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.