1.5k citations · 1.6k across the 14 of their papers we have counts for
7 papers · 1 filter
Benchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
Haochen Zhao, Minsheng Hao, Zheng Li +8
Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence. Recent…
TimeThink: Reasoning with Time for Video LLMs
Handong Li, Longteng Guo, Zikang Liu +8
Video reasoning requires models to identify and verify temporally localized evidence within long video sequences. Recent Video Large Language Models (Video-LLMs) have shown promisi…
A Task-State Representation for Long-Horizon Mobile GUI Agents
Yujie Zheng, Zikang Liu, Xin Zhao +1
While long-horizon mobile GUI agents typically rely on thought-action-observation loops, they struggle to separate persistent task states from transient screen observations. As exe…
Towards Long-horizon Agentic Multimodal Search
Yifan Du, Zikang Liu, Jinbiao Peng +5
Multimodal deep search agents have shown great potential in solving complex tasks by iteratively collecting textual and visual evidence. However, managing the heterogeneous informa…
Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility
Zikang Liu, Peilan Xu
Generative answer engines expose content through selective citation rather than ranked retrieval, fundamentally altering how visibility is determined. This shift calls for new opti…
AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding
Handong Li, Zikang Liu, Longteng Guo +10
Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fine-grained perception throug…
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
Yifan Li, Yukai Gu, Yingqian Min +6
Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation…