LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a new security concern: attackers may manipulate agents to induce group polarization. Existing methods manipulate agent prompts or construct echo chambers, both of which are difficul...
Haoran Bu, Zejian Chen, Li-Tian Zhang et al.· 0 citations
A holistic framework based on learnable information gain, which measures how much novel, parameterizable information a round provides relative to the previous round, and proposes ATRI (Adaptive Training Regulation via Information-gain), which reweights samples within a round and halts training across rounds when inform...
Chen-Xu Wang, Chao-Zhuo Li, Xin-Ze Shi et al.· 0 citations
EvoBreak is an experience-conditioned sequential attack that operates through individually benign attack-stage tasks and induced experiences, revealing benign experience composition as a persistent attack surface in self-evolving agents.
Bing-Yu Yan, Xiao-Ming Zhang, Chao-Zhuo Li et al.· 2 citations
MAGER is a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning, enabling frozen LLMs to perform structure-aware veracity reasoning and introduces a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen...
Zi-Yi Zhou, Xiao-Ming Zhang, Hui Pang et al.· 0 citations
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.
Longzhu He, Peng Tang, Chaozhuo Li et al.· IEEE Transactions on Knowled...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
