1 citations · 1 across the 7 of their papers we have counts for
8 papers
LenGuard-GPC: Length Guarding with Guided-Prompt Consistency for Spatial Reasoning Reinforcement Learning
Xingjian Tao, Yiwei Wang, Yujun Cai +2
Multi-view spatial reasoning requires vision-language models to compare visual evidence across images, align object correspondences, and infer spatial relations over long visual co…
EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval
Yifan Song, Xingjian Tao, Zhicheng Yang +2
Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recent lightweight approaches reduce…
ViewFusion: Structured Spatial Thinking Chains for Multi-View Reasoning
Xingjian Tao, Yiwei Wang, Yujun Cai +2
Multi-view spatial reasoning remains difficult for current vision-language models. Even when multiple viewpoints are available, models often underutilize cross-view relations and i…
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
Yifan Song, Fenglin Yu, Yihong Luo +4
Incomplete node features are ubiquitous in real-world scenarios such as user profiling and cold-start recommendation, which severely hinders the practical deployment of graph learn…
Mitigating Coordinate Prediction Bias from Positional Encoding Failures
Xingjian Tao, Yiwei Wang, Yujun Cai +3
While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant challenge, particularly as high-resolutio…
Understanding GUI Agent Localization Biases through Logit Sharpness
Xingjian Tao, Yiwei Wang, Yujun Cai +2
Multimodal large language models (MLLMs) have enabled GUI agents to interact with operating systems by grounding language into spatial actions. Despite their promising performance,…
Are LLMs Really Not Knowledgeable? Mining the Submerged Knowledge in LLMs' Memory
Xingjian Tao, Yiwei Wang, Yujun Cai +2
Large language models (LLMs) have shown promise as parametric knowledge bases, but often underperform on question answering (QA) tasks due to hallucinations and uncertainty. While…
Energy-Calibrated VAE with Test Time Free Lunch
Yihong Luo, Siya Qiu, Xingjian Tao +2
In this paper, we propose a novel generative model that utilizes a conditional Energy-Based Model (EBM) for enhancing Variational Autoencoder (VAE), termed Energy-Calibrated VAE (E…