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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.
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.
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.
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.
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.
Haoyi Zhu, Haozhe Liu, Yuyang Zhao +6 authors
SANA-WM is an efficient 2.6B-parameter world model that generates high-fidelity 720p videos with precise camera control, achieving industrial-level quality while significantly reducing computational requirements through hybrid attention, dual-camera branches, two-stage generation, and robust annotation pipelines.
Pengqi Lu
Deep diffusion transformers face structural instability at extreme depths due to mean-dominated collapse triggered by mean mode screaming, which is mitigated through mean-variance split residuals that maintain stable training while preserving performance.
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.
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.
Zhaowei Wang, Lishu Luo, Haodong Duan +9 authors
Long-context continued pre-training enhances vision-language models' ability to handle extended documents while maintaining performance across diverse contexts through strategic data mixture design.
Keming Wu, Zuhao Yang, Kaichen Zhang +24 authors
Visual generation models need to advance beyond appearance synthesis to incorporate structural, dynamic, and causal understanding through a five-level taxonomy spanning from atomic to world-modeling generation.
Hongcan Guo, Qinyu Zhao, Yian Zhao +8 authors
Cola DLM presents a hierarchical latent diffusion language model that uses text-to-latent mapping, global semantic prior modeling, and conditional decoding to achieve efficient text generation with flexible non-autoregressive inductive bias.
Houyuan Chen, Hong Li, Xianghao Kong +8 authors
UniVidX is a unified multimodal framework that uses video diffusion model priors for versatile video generation through stochastic condition masking, decoupled gated LoRA, and cross-modal self-attention mechanisms.
Hongru Hou, Tiehua Mei, Denghui Geng +5 authors
Proactive recommender systems using reinforcement learning face challenges with gradient estimation bias and variance, which are addressed through stepwise reward centering and position-specific advantage estimation mechanisms.
Fancy Kong, Congjie Zheng, Murphy Zhuang +8 authors
Generative UI models enable personal agents to synthesize dynamic interfaces with lightweight executable actions for enhanced interaction beyond text-only formats.
Guijin Son, Seungone Kim, Catherine Arnett +65 authors
A new 439-problem mathematical benchmark created by mathematicians demonstrates significant gaps in advanced reasoning capabilities of leading language models, particularly in identifying ill-posed problems.
Bang Liu, Yongfeng Gu, Jiayi Zhang +26 authors
Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another. As these systems scale, the bottleneck shifts away from raw model capability toward coordination. Agents need to form reliable relationships, organize multi-agent work, exchange value, support an AI economy, and stay safe and accountable under real-world oversight. This paper introduces the Foundation Protocol (FP), a graph-first coordination layer for an emerging human-AI society. FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations, and supports native multi-party organization and event-based collaboration. It also provides economic primitives for metering, receipts, and settlement, and treats policy, provenance, and audit as first-class concerns. FP is designed to wrap and bridge existing protocols rather than replace them, enabling incremental adoption while reducing integration and governance overhead. The aim is to keep autonomous agency composable while keeping accountability non-negotiable, so that coordination itself can become shared infrastructure for a human-AI society that is open, pluralistic, and governable.
Hongyi Liu, Haoyan Yang, Tao Jiang +3 authors
SkillsVote is a governance framework for long-horizon LLM agents that manages reusable skills through structured collection, recommendation, and evolution processes.
Jinheon Baek, Soyeong Jeong, Sangwoo Park +5 authors
OmniRetrieval is a framework that handles diverse knowledge sources by identifying appropriate repositories and dispatching native queries to their respective execution engines, outperforming single-source approaches across multiple dataset types.
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