activity
20242026
most citedRGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

3 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Can Vision Replace Text in Working Memory? Evidence from Spatial n-Back in Vision-Language Models

Sichu Liang, Hongyu Zhu, Wenwen Wang +1

Working memory is a central component of intelligent behavior, providing a dynamic workspace for maintaining and updating task-relevant information. Recent work has used n-back tas…

cs.CV2025

Evading Data Provenance in Deep Neural Networks

Hongyu Zhu, Sichu Liang, Wenwen Wang +3

Modern over-parameterized deep models are highly data-dependent, with large scale general-purpose and domain-specific datasets serving as the bedrock for rapid advancements. Howeve…

cs.CV2025

Revisiting Data Auditing in Large Vision-Language Models

Hongyu Zhu, Sichu Liang, Wenwen Wang +5

With the surge of large language models (LLMs), Large Vision-Language Models (VLMs)--which integrate vision encoders with LLMs for accurate visual grounding--have shown great poten…

cs.CL20253 cited

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

Sichu Liang, Linhai Zhang, Hongyu Zhu +3

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical kno…

cs.CR2024

Efficient and Effective Model Extraction

Hongyu Zhu, Wentao Hu, Sichu Liang +3

Model extraction aims to create a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor t…