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Qunzhong Wang

2 accepted papers

2026

VR-Thinker: Boosting Multimodal Reward Models through Think with Image Reasoning

ICML 2026poster

Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: **(1)** visual inputs consume large context budgets, forcing fewer frames and causing a loss of details; and **(2)** all visua…

Cited by 0SourceScholar
2025

Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis

ICML 2025poster

In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to original graphs without requiring retraining of pre-trained graph models across various applications. This novel paradigm, shifting from the traditional…

Cited by 3SourcePDFScholar