collaborators

11 papers

cs.PL2026

When Can Agents Safely Checkpoint, Fork, Restore, and Merge? Exact Checking for Execution Edits

Yusheng Zheng, Xiaoyu Song, Yanpeng Hu +3

Agent runtimes can Checkpoint an execution, Fork it, Restore a checkpoint, or Merge branches without restarting a task. We call these operations execution edits, with Checkpoint re…

cs.OS2026

Characterizing and Bridging the Diagnostic Gap in eBPF Verifier Rejections

Yusheng Zheng, Zhengjie Ji, Weichen Tao +5

eBPF lets developers run custom programs inside the Linux kernel, where a verifier proves each program safe. However, when the verifier rejects a program, the unclear error makes r…

cs.LG2026

MarginGate: Sparse Margin-Triggered Verification for Batch-Invariant LLM Inference

Kexin Chu, Yang Zhou, Wei Zhang

Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larger batch. Existing fixes use…

cs.CR2026

ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore

Yusheng Zheng, Yiwei Yang, Wei Zhang +1

LLM agent frameworks increasingly offer checkpoint-restore for error recovery and exploration, advising developers to make external tool calls safe to retry. This advice assumes th…

cs.CR2026

Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference

Kexin Chu, Zecheng Lin, Dawei Xiang +7

Global KV-cache sharing is an effective optimization for accelerating large language model (LLM) inference, yet it introduces an API-visible timing side channel that lets adversari…

cs.PF2026

Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference

Kexin Chu, Dawei Xiang, Zixu Shen +3

Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited G…