activity
20242026
most citedGSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis

1 citations · 1 across the 8 of their papers we have counts for

collaborators

14 papers

cs.AI2026

OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking

Yiding Ma, Chengyun Ruan, Kaibo Huang +2

Large language models are moving from static text generators toward real-world decision-support systems, where forecasting is a composite capability that links information gatherin…

cs.AI2026

ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry

Wansheng Wu, Kaibo Huang, Yukun Wei +2

As generative artificial intelligence evolves, autonomous agent networks present a powerful paradigm for interactive covert communication. However, because agents dynamically updat…

cs.MA2026

EvoCorps: An Evolutionary Multi-Agent Framework for Depolarizing Online Discourse

Ning Lin, Haolun Li, Mingshu Liu +5

Polarization in online discourse erodes social trust and accelerates misinformation, yet technical responses remain largely diagnostic and post-hoc. Current governance approaches s…

cs.CR2026

AgentMark: Utility-Preserving Behavioral Watermarking for Agents

Kaibo Huang, Jin Tan, Yukun Wei +5

LLM-based agents are increasingly deployed to autonomously solve complex tasks, raising urgent needs for IP protection and regulatory provenance. While content watermarking effecti…

cs.CL2026

Robust Uncertainty Quantification for Factual Generation of Large Language Models

Yuhao Zhang, Zhongliang Yang, Linna Zhou

The rapid advancement of large language model(LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge…

cs.MA2025

CreditXAI: A Multi-Agent System for Explainable Corporate Credit Rating

Yumeng Shi, Zhongliang Yang, Yisi Wang +1

In the domain of corporate credit rating, traditional deep learning methods have improved predictive accuracy but still suffer from the inherent 'black-box' problem and limited int…