13 papers
UniOPSD: Unifying Outcome and Hindsight Feedback for Agentic Reinforcement Learning
Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai +8
Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment acr…
SIPO: Selective-Inference Policy Optimization for Tree-Structured Agentic RL
Zenghuang Fu, Ningqi Chen, Mingda Jia +10
Tree-structured reinforcement learning trains search agents by comparing alternative continuations and propagating terminal rewards to intermediate decisions. Adaptive expansion, h…
EchoWM: Open and Enterable Omnimodal World Models
Songchun Zhang, Yaowei Li, Junhao Zhuang +19
We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, environmental sound, music an…
VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System
Haodong Li, Tianfei Ren, Xiaoxiao Ma +25
Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be i…
CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents
Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai +6
Large language models can improve with reinforcement learning for search agents, yet existing self play agents repeatedly generate tasks while discarding the knowledge gained durin…
Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember
Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai +6
Self-play agents can generate training problems without questions from target benchmarks, but their curricula lack persistent state: failures affect gradients yet do not explicitly…
MultiWorld: Scalable Multi-Agent Multi-View Video World Models
Haoyu Wu, Jiwen Yu, Yingtian Zou +1
Video world models have achieved remarkable success in simulating environmental dynamics in response to actions by users or agents. They are modeled as action-conditioned video gen…
Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers
Haoyu Wu, Qingcheng Zeng, Kaize Ding
Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines, where accurately retrieving factual information is crucial for maintaining system trus…
Oedipus and the Sphinx: Benchmarking and Improving Visual Language Models for Complex Graphic Reasoning
Jianyi Zhang, Xu Ji, Ziyin Zhou +5
Evaluating the performance of visual language models (VLMs) in graphic reasoning tasks has become an important research topic. However, VLMs still show obvious deficiencies in simu…
Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He +4
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture…
MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
Junliang Guo, Yang Ye, Tianyu He +4
World modeling is a crucial task for enabling intelligent agents to effectively interact with humans and operate in dynamic environments. In this work, we propose MineWorld, a real…
Fast Autoregressive Video Generation with Diagonal Decoding
Yang Ye, Junliang Guo, Haoyu Wu +5
Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, parti…
VideoDPO: Omni-Preference Alignment for Video Diffusion Generation
Runtao Liu, Haoyu Wu, Zheng Ziqiang +4
Recent progress in generative diffusion models has greatly advanced text-to-video generation. While text-to-video models trained on large-scale, diverse datasets can produce varied…