activity
20232026
most citedSafe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems

6 citations · 7 across the 13 of their papers we have counts for

collaborators

15 papers

cs.CR2026

Steering LLM Viewpoints through Fabricated Evidence Injection

Xi Yang, Chang Liu, Zhenglin Huang +4

As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cogni…

cs.AI2026

A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions

Yu Zhang, Dongjiang Zhuang, Qu Zhou +4

Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific six- or eight-digit code unde…

cs.MA20266 cited

Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems

Tianxiao Li, Yixing Ma, Haiquan Wen +4

Modern LLM based agents are no longer passive text generators. They read repositories, call tools, browse the web, execute code, maintain memory, communicate with other agents, and…

cs.CV2026

Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection

Tianxiao Li, Zhenglin Huang, Haiquan Wen +10

Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are constrained by their single-…

cs.CL2026

Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models

Zhuoyun Li, Boxuan Wang, Jinwei Hu +6

Large language models are sensitive to minor prompt perturbations, yet existing robustness methods usually enforce consistency at the whole-sequence level. This holistic view can h…

cs.CV2025

Rethinking Cross-Generator Image Forgery Detection through DINOv3

Zhenglin Huang, Jason Li, Haiquan Wen +7

As generative models become increasingly diverse and powerful, cross-generator detection has emerged as a new challenge. Existing detection methods often memorize artifacts of spec…