Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target short videos, extending them to long videos remains challenging, primarily due to limited context windows of current video understanding models. A naive solution is to...
Xinlong Chen, Wei-Hong Lin, Yuan-Xing Zhang et al.· 0 citations
Preference optimization is a standard alignment method for generative models, yet extending it to continuous-time dynamics remains non-trivial. In flow matching, reward-driven updates modify transport trajectories without an inherent constraint to the pretrained data manifold and can move terminal samples off the pretr...
Yan-Sen Han, Shengyi Liao, Yuan-Xing Zhang et al.· 0 citations
OmniPack is proposed, a training-free framework that coordinates structural compression before the LLM with task-relevant semantic refinement within the LLM, and consistently achieves the best performance-efficiency trade-off across diverse retention ratios, outperforming all existing methods.
Wan-Shun Su, Yang Shi, Fei Liu et al.· 2 citations
The Thinking Reward Model (TRM), a paradigm that explicitly determines what matters for each case before judging how well the candidate performs, is proposed, which formulates case-adaptive rubrics, performs rubric-guided assessment, and produces fine-grained pointwise rewards.
Xue-Yuan Bai, Zhen-Chen Tang, Yang Shi et al.· 0 citations
This work introduces SPACE (Subject-centric Poses, Appearance, and Characteristics Evaluation), a benchmark designed to evaluate subject-centered spatial understanding, and designs a rubric-based reward for Group Relative Policy Optimization that explicitly penalizes structurally critical spatial errors during alignmen...
Bozhou Li, Jia-Hang Zhang, Yue Ding et al.· 0 citations
This work introduces HiRAE (Hierarchical Representation Autoencoder), which learns a hierarchical fusion framework over the full encoder hierarchy to improve reconstruction fidelity while maintaining compatibility with generative modeling.
Xuan-Yu Zhu, Yan Bai, Yang Shi et al.· 0 citations
Achieving high-quality, cinematic results in text-to-video generation remains challenging for non-experts, whose prompts often lack professional narrative and creative design. We propose SkillPE, a prompt engineering (PE) framework that evolves reusable cinematic skills from expert-authored seeds. SkillPE represents sh...
Yan-Wei Huang, Min Zhu, Shu-Jie Li et al.· 0 citations
Flux-OPD is proposed, an OPD paradigm that uses evolving contexts as in-training supervision to capture task preferences in open-ended domains and outperforms existing OPD paradigms, highlighting the potential to combine teacher supervision with evolving contexts.
This work proposes Vid-PRE (Video Prompt Reasoner and Enhancer), a model-agnostic prompt rewriter that offloads the cognitive burden of reasoning to a dedicated VLM, and introduces VWG-Bench, a comprehensive benchmark spanning 9 reasoning dimensions and 38 fine-grained tasks.
Meng Luo, Yi-Chen Liu, Jia-Hao Wang et al.· 1 citation
This work introduces FlowCPO, an offline forward-KL objective that uses both preferred and dispreferred samples without online rollouts and shows under explicit regularity conditions that the forward-KL objective is bounded by a contrastive flow matching loss, yielding a tractable surrogate on fixed data.
Yan-Sen Han, Shengyi Liao, Peng Sun et al.· 1 citation
Human evaluation confirms that RefCaptioner achieves the best overall performance among the open-source models while remaining competitive on standard video captioning benchmarks, and human evaluation further confirms that its captions are preferred by annotators and enable more source-faithful video reconstruction wit...
Teng-Fei Liu, Yang Shi, Yu-Ran Wang et al.· arXiv.org· 0 citations
Beacon, a novel agentic visual reasoning model trained with supervised fine-tuning and reinforcement learning, is proposed, which achieves the highest average score among the evaluated open-source models and ranks first on 11 benchmarks.
Qi-Xun Wang, Yang Shi, Le-Tian Cheng et al.· arXiv.org· 1 citation
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