most citedMitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents

Wenqi Liu, Shijie Ma, Yunxiao Wang +21

Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video and its parametric memory. While…

cs.CL2026

Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue

Junan Hu, Shudan Guo, Wenqi Liu +2

Large Language Models demonstrate outstanding performance in many language tasks but still face fundamental challenges in managing the non-linear flow of human conversation. The pr…

cs.CV2026

VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos

Wenqi Liu, Yunxiao Wang, Shijie Ma +14

In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance and increased hallucinations. To addres…

cs.AI2025★ 2 cited

Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization

Wenqi Liu, Xuemeng Song, Jiaxi Li +4

Direct Preference Optimization (DPO) has emerged as an effective approach for mitigating hallucination in Multimodal Large Language Models (MLLMs). Although existing methods have a…

cs.CV2025

TIME: Temporal-Sensitive Multi-Dimensional Instruction Tuning and Robust Benchmarking for Video-LLMs

Yunxiao Wang, Meng Liu, Wenqi Liu +7

Video large language models have achieved remarkable performance in tasks such as video question answering, however, their temporal understanding remains suboptimal. To address thi…