7 papers
VIPER-MCP: Detecting and Exploiting Taint-Style Vulnerabilities in Model Context Protocol Servers
Pengyu Sun, Qishu Jin, Enhao Huang +4
Model Context Protocol (MCP) has emerged as a standard interface for connecting LLM agents to external tools. Because MCP servers expose privileged operations such as shell executi…
PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVR
Yiqi Zhang, Fangzheng Jiao, Tian Tang +13
Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering rapid exploration of new algor…
Learning to Wait: Synchronizing Agents with the Physical World
Yifei She, Ping Zhang, He Liu +6
Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \textit{Temporal G…
ReSpec: Towards Optimizing Speculative Decoding in Reinforcement Learning Systems
Qiaoling Chen, Zijun Liu, Peng Sun +6
Adapting large language models (LLMs) via reinforcement learning (RL) is often bottlenecked by the generation stage, which can consume over 75\% of the training time. Speculative d…
BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping
Zhiheng Xi, Xin Guo, Yang Nan +18
Reinforcement learning (RL) has recently become the core paradigm for aligning and strengthening large language models (LLMs). Yet, applying RL in off-policy settings--where stale…
Metadata-Guided Adaptable Frequency Scaling across Heterogeneous Applications and Devices
Jinqi Yan, Fang He, Qianlong Sang +5
Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate f…