9 papers
ProtoArena: Can AI Agents Discover and Exploit Real-World Cross-Component Protocol Vulnerabilities?
Fasheng Miao, Zhaoyu Hu, Xiang Li +5
Large language model (LLM) agents are increasingly used for vulnerability analysis, yet existing benchmarks provide limited coverage of exploitation that depends on interactions am…
SAKI: Maximal-Coupling-Routed Teacher Supervision for On-Policy Distillation
Miteto Wei, Xiaohan Wang, Zehao Chen +7
On-policy distillation (OPD) reduces train-test state mismatch by training a student on its own generated trajectories, but weak students may visit teacher-misaligned prefixes wher…
Tool Retrievers Are Underestimated: Annotation Expansion Reveals True Capability
Yanyu Zhu, Chenheng Zhang, Shaoshen Chen +8
In open-world scenarios with massive and evolving tool repositories, tool-augmented large language models rely on a retriever to surface relevant tools for a given query. Because s…
ATLAS: Dual-Horizon Diagnostic Evaluation for Tool-Use Agents in Meituan Xiaotua
Wei Chen, Peilun Zhou, Zhaoyu Hu +8
LLM-based assistants on web-scale service platforms increasingly address local-service needs through multi-step planning and tool use. Under dynamic business conditions and evolvin…
RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
Wenwen Zeng, Jinhui Zhang, Hao Chen +10
The integration of Large Language Model (LLM) agents is transforming recommender systems from simple query-item matching towards deeply personalized and interactive recommendations…
XekRung Technical Report
Jiutian Zeng, Junjie Li, Chengwei Dai +13
We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis p…
MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation
Xianyang Qi, Yuan Tian, Zhaoyu Hu +4
Industrial recommender systems critically depend on high-quality ranking models. However, traditional pipelines still rely on manual feature engineering and scenario-specific archi…
Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation
Zhaoyu Hu, Jianyang Wang, Hao Guo +6
In the context of the booming digital economy, recommendation systems, as a key link connecting users and numerous services, face challenges in modeling user behavior sequences on…
When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation
Hongxiang Lin, Hao Guo, Zeshun Li +6
Traditional recommendation systems tend to trap users in strong feedback loops by excessively pushing content aligned with their historical preferences, thereby limiting exploratio…