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Bin Wu, Mengqi Huang, Shaojin Wu +4 authors
Stream-R1 improves video diffusion model distillation by adaptively weighting supervision based on reliability and perplexity, enhancing visual quality, motion quality, and text alignment without additional computational overhead.
Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65 authors
RLDX-1 is a general-purpose robotic policy for dexterous manipulation that integrates heterogeneous modalities through a Multi-Stream Action Transformer architecture, demonstrating superior performance in complex real-world tasks compared to existing vision-language-action models.
Zhuofeng Li, Haoxiang Zhang, Cong Wei +16 authors
Direct corpus interaction enables more effective agentic search by allowing agents to query raw text directly, outperforming traditional retrieval methods in complex tasks.
Kangning Zhang, Shuai Shao, Qingyao Li +8 authors
Multimodal procedural knowledge frameworks enable visual agents to leverage external reusable skills through structured representations combining text, state cards, and visual keyframes, improving decision-making in complex environments.
Bing Zhao, Chenfei Wu, Deqing Li +72 authors
Qwen-Image-2.0 is an advanced image generation model that combines high-fidelity synthesis with precise editing capabilities through a unified framework using Qwen3-VL as condition encoder and Multimodal Diffusion Transformer for joint modeling.
Zhengxi Lu, Zhiyuan Yao, Zhuowen Han +8 authors
SDAR enhances reinforcement learning for multi-turn agent training by integrating self-distillation through a sigmoid gate that selectively strengthens positive token-level guidance while mitigating negative teacher rejections.
Yukang Chen, Luozhou Wang, Wei Huang +13 authors
LongLive-2.0 presents an NVFP4-based parallel infrastructure for long video generation that addresses training and inference bottlenecks through sequence-parallel autoregressive training and diffusion model tuning.
Yaorui Shi, Yuxin Chen, Zhengxi Lu +6 authors
Skill1 is a unified framework that trains a single policy to simultaneously evolve skill selection, utilization, and distillation capabilities using a shared task-outcome objective, demonstrating superior performance over existing baselines in complex task environments.
Chao Xu, Maohua Li, Qirui Li +9 authors
Diffusion Transformers suffer from inefficient cross-layer information flow that traditional residual connections cannot address, prompting the introduction of a learnable, timestep-adaptive routing mechanism that improves training efficiency and model quality.
Dong Chen, Fangyun Wei, Ziyu Wan +18 authors
Lens is a compact 3.8B-parameter text-to-image model achieving superior performance with reduced training compute through dense caption datasets, multi-resolution batching, efficient architecture, and optimization techniques.
Yijing Tu, Shaojin Wu, Mengqi Huang +4 authors
Stream-T1 enables efficient test-time video generation through streaming synthesis with temporal guidance, achieving improved consistency and quality over existing diffusion-based methods.
Haoran Zhang, Luxin Xu, Zhilin Wang +11 authors
Proactive assistance in personal agent systems requires identifying hidden user intents through sustained multi-turn interactions, which current benchmarks fail to adequately evaluate.
Kaining Ying, Hengrui Hu, Siyu Ren +6 authors
WBench presents a comprehensive multi-turn benchmark for evaluating interactive world models across five dimensions using 289 test cases and 1,058 interaction turns with diverse scenarios and interaction types.
Shuang Chen, Kaituo Feng, Hangting Chen +7 authors
OpenSearch-VL presents an open-source framework for training advanced multimodal search agents using reinforcement learning, featuring specialized data curation, diverse tool environments, and a novel training algorithm that improves performance across multiple benchmarks.
Yuchao Gu, Guian Fang, Yuxin Jiang +4 authors
AnyFlow introduces a novel any-step video diffusion distillation framework that improves upon consistency distillation by optimizing full ODE sampling trajectories through flow-map transition learning and backward simulation techniques.
Zhen Fang, Wenxuan Huang, Yu Zeng +8 authors
Flow-OPD addresses limitations in Flow Matching text-to-image models through a two-stage alignment approach combining on-policy distillation and manifold anchor regularization, achieving significant improvements in generation quality and alignment metrics.
Yanke Zhou, Yiduo Li, Hanlin Tang +6 authors
RTPurbo leverages intrinsic sparsity in full-attention LLMs to achieve efficient long-context inference with minimal training overhead, enabling significant speedups while maintaining near-lossless accuracy.
Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero +3 authors
Pairwise ranking prompting is reformulated as active learning from noisy comparisons, with improved rankers that enhance ranking quality under call constraints and address position bias through a randomized oracle.
Min Zhao, Hongzhou Zhu, Kaiwen Zheng +6 authors
A novel causal consistency distillation method enables efficient frame-wise video generation with reduced latency and improved quality compared to existing chunk-wise approaches.
Minki Kang, Shizhe Diao, Ryo Hachiuma +4 authors
Agents using vision-language models with extended reasoning face challenges in tool utilization, which are addressed through AXPO, a method that improves performance by optimizing thinking prefixes and tool call resampling.
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