The proposed IDUnlearn-Bench is the first benchmark for individual-level multimodal unlearning in VLMs, which represents each individual as connected multimodal evidence and evaluates four task families: attribute access, identity access, identity binding, and identity reconstruction.
Xiong-Tao Sun, Hui Li, Tian-Tong Wu et al.· 0 citations
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it si...
Yu-Rong Hao, Wen Zhou, Guo-Wei Guan et al.· 0 citations
FraudBench is a multimodal benchmark for detecting AI-generated fraudulent refund evidence and shows that current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates far below the 50\% baseline on most generator subsets.
It is shown that schema-defined outputs change but do not eliminate prompt-injection risk, highlighting the need to evaluate how untrusted content influences choices within the allowed action set.
Tian-Tong Wu, Wei Yang Bryan Lim· 12 citations· ⚡1
REFLEX, an agent architecture that uses Jev as a fast, typed decision layer and calls a strong LLM when confidence is low, or generation is required, is studied, identifying when selective control with Jev can reduce computation and where its benefits are limited.
Results indicate that current LLMs are substantially more reliable at detecting broad collaboration opportunities than at identifying their specific types and role directions.
Tian Du, Tian-Tong Wu, Ya-Fei Wang et al.· 0 citations
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