activity
20242026
most citedHetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering

2 citations · 3 across the 11 of their papers we have counts for

collaborators

12 papers

cs.AI2026

Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation

Runsong Jia, Zhen Fang, Mengjia Wu +2

Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detect…

cs.SI2026★ 1 cited

Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering

Runsong Jia, Mengjia Wu, Ying Ding +2

Academic question answering requires reasoning over heterogeneous scholarly graphs, where queries range from simple attribute lookups to multi-hop inference across author--paper--v…

cs.IR2026

Understanding before verifying: Claim normalization for automated citation verification

Yifan He, Mengjia Wu, Siming Deng +1

Citation accuracy has been studied for decades because of its importance to research reliability. Content-level citation verification assesses the reliability of scholarly claims.…

cs.CV2026

Grid2Matrix: Revealing Digital Agnosia in Vision-Language Models

Yunkai Zhang, Linda Li, Yingxin Cui +5

Vision-Language Models (VLMs) excel on many multimodal reasoning benchmarks, but these evaluations often do not require an exhaustive readout of the image and can therefore obscure…

cs.CV2026

Arbitration Failure, Not Perceptual Blindness: How Vision-Language Models Resolve Visual-Linguistic Conflicts

Farhad Nooralahzadeh, Omid Rohanian, Yi Zhang +2

When a Vision-Language Model (VLM) sees a blue banana and answers "yellow", is the problem of perception or arbitration? We explore the question in ten VLMs with various sizes and…

cs.CL2026

From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation

Mingfei Lu, Yi Zhang, Mengjia Wu +1

Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex t…

cs.IR2026

Large language models for post-publication research evaluation: Evidence from expert recommendations and citation indicators

Mengjia Wu, Yi Zhang, Robin Haunschild +1

Assessing the quality of scientific research is essential for scholarly communication, yet widely used approaches face limitations in scalability, subjectivity, and time delay. Rec…

cs.SI2025

From Newborn to Impact: Bias-Aware Citation Prediction

Mingfei Lu, Mengjia Wu, Jiawei Xu +6

As a key to accessing research impact, citation dynamics underpins research evaluation, scholarly recommendation, and the study of knowledge diffusion. Citation prediction is parti…

cs.SI2025

AI-Enhanced Multi-Dimensional Measurement of Technological Convergence through Heterogeneous Graph and Semantic Learning

Siming Deng, Runsong Jia, Chunjuan Luan +2

Technological convergence refers to the phenomenon where boundaries between technological areas and disciplines are increasingly blurred. It enables the integration of previously d…

cs.SI2025★ 2 cited

HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering

Runsong Jia, Mengjia Wu, Ying Ding +2

Academic question answering (QA) in heterogeneous scholarly networks presents unique challenges requiring both structural understanding and interpretable reasoning. While graph neu…

cs.SI2024

Two Birds with One Stone: Improving Rumor Detection by Addressing the Unfairness Issue

Junyi Chen, Mengjia Wu, Qian Liu +2

The degraded performance and group unfairness caused by confounding sensitive attributes in rumor detection remains relatively unexplored. To address this, we propose a two-step fr…

cs.AI2024

Responsible AI: Portraits with Intelligent Bibliometrics

Yi Zhang, Mengjia Wu, Guangquan Zhang +1

Shifting the focus from principles to practical implementation, responsible artificial intelligence (AI) has garnered considerable attention across academia, industry, and society…