3 citations · 3 across the 9 of their papers we have counts for
10 papers
CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems
Ziyang Ma, Dingyi Zhang, Sichu Liang +4
Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over single agent systems, they lead…
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…
When KV Cache Reuse Fails in Multi-Agent Systems: Cross-Candidate Interaction is Crucial for LLM Judges
Sichu Liang, Zhenglin Wang, Jiajia Chu +3
Multi-agent LLM systems routinely generate multiple candidate responses that are aggregated by an LLM judge. To reduce the dominant prefill cost in such pipelines, recent work advo…
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…
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…
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…
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…
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
Hongyu Zhu, Sichu Liang, Wentao Hu +3
With the rise of Machine Learning as a Service (MLaaS) platforms,safeguarding the intellectual property of deep learning models is becoming paramount. Among various protective meas…
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
Hongyu Zhu, Sichu Liang, Wentao Hu +4
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision…
An Input-Aware Mimic Defense Theory and its Practice
Jiale Fu, Yali Yuan, Jiajun He +3
The current security problems in cyberspace are characterized by strong and complex threats. Defenders face numerous problems such as lack of prior knowledge, various threats, and…