1.5k citations · 1.6k across the 14 of their papers we have counts for
15 papers
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…
PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents
Zikang Liu, Junyi Li, Wayne Xin Zhao +3
Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) promise human-like interaction with software applications, yet long-horizon tasks remain c…
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
Zikang Liu, Kun Zhou, Wayne Xin Zhao +3
Visual instruction tuning has become the predominant technology in eliciting the multimodal task-solving capabilities of large vision-language models (LVLMs). Despite the success,…
Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
Yifan Du, Zikang Liu, Yifan Li +7
Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also…
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Xingjian He, Sihan Chen, Fan Ma +7
Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. However, there is limited…
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
Zikang Liu, Sihan Chen, Longteng Guo +3
Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answerin…
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li +19
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…
A Survey of Vision-Language Pre-Trained Models
Yifan Du, Zikang Liu, Junyi Li +1
As transformer evolves, pre-trained models have advanced at a breakneck pace in recent years. They have dominated the mainstream techniques in natural language processing (NLP) and…