9 papers
Refusal is Not Safety! Benchmarking Latent Safety Risks of LLM-Driven Content Humorization
Yu Cui, Ruiqing Yue, Tingyu Li +6
Safety defenses for large language models (LLMs) have been extensively studied, with existing approaches focusing on attack detection and refusal mechanisms. Such fixed-form direct…
Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models
Yongqin Zeng, Sicheng Pan, Jiale Wang +4
Sparsely activated Mixture-of-Experts (MoE) language models contain substantial structured redundancy among routed experts, but pruning them without downstream calibration data rem…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
MiniMax, :, Aili Chen +219
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
An Extensible and Verifiable Language for Query Rewrite Rules
Sicheng Pan, Shuxian Wang, Wesley Zheng +3
Logical query plan rewriting transforms a relational database query into an equivalent but more efficient form and is crucial to the performance of database-backed applications. In…
Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing
Yu Cui, Ruiqing Yue, Hang Fu +6
With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue.…
Adaptive Multi-Objective Tiered Storage Configuration for KV Cache in LLM Service
Xianzhe Zheng, Zhengheng Wang, Ruiyan Ma +17
The memory-for-computation paradigm of KV caching is essential for accelerating large language model (LLM) inference service, but limited GPU high-bandwidth memory (HBM) capacity m…