3 papers
cs.AI2026
When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs
Yifan Zeng, Yiran Wu, Yaolun Zhang +4
Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that…
cs.CR2026
CachePrune: Privacy-Aware and Fine-Grained KV Cache Sharing for Efficient LLM Inference
Guanlong Wu, Zhaohan li, Yao Zhang +4
Large Language Models (LLMs) rely on Key-Value (KV) caching to accelerate inference, and many serving systems further share the KV cache across users' requests to reduce redundant…
cs.CR2025
HORAM: A High-Performance Hierarchical Doubly Oblivious RAM
Leqian Zheng, Zheng Zhang, Wentao Dong +3
The combination of Oblivious RAM (ORAM) with Trusted Execution Environments (TEE) has found numerous real-world applications due to their complementary nature. TEEs alleviate the p…