LLM agents retrieve untrusted content and act through tools, creating indirect prompt-injection risks that can cause unauthorized actions or persistent state changes. Existing automated red-teaming largely optimizes payloads for pre-specified scenarios, leaving latent vulnerabilities across the agent's behavior space u...
Peng-Fei He, Deepanjan Mitra, Vishesh Sharma et al.· 0 citations
Knowledge tracing (KT) aims to predict learners’ mastery levels of knowledge components (KCs) or test items based on their interaction records with educational content. Despite significant advancements in KT models, such as RNN-based sequence models and Transformer-based attention models, a critical limitation persists...
Mingliang Hou, Zitao Liu, Ren-Qiang Luo et al.· ACM Transactions on Informat...· 0 citations
Modern recommendation systems on social media platforms such as Meta must model complex social relationships, including friendships, group memberships, and creator interactions, alongside massive and heterogeneous content such as text and video. Traditional recommendation models, however, often omit these signals or tr...
Haoyu Han, Yuming Liu, Lei Huang et al.· 0 citations
This work introduces BONSAI : Branching-Optimized Node Structure for Adaptive Identifiers, a novel framework that co-designs textual term IDs and their underlying decoding trie that satisfies two desirable properties for a highly performant trie.
This work comprehensively investigates computation-efficient strategies to speed up latent adversarial training from two complementary perspectives, and reduces per-step adversarial-training FLOPs by 48.1% while requiring only 0.0118% trainable parameters.
