1 citations · 1 across the 11 of their papers we have counts for
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EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation
Zeru Shi, Jindong Jiang, Ryo Hachiuma +10
Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance. However, using these models for every step of a long-horizon…
AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems
Boxuan Zhang, Jianing Zhu, Zeru Shi +2
LLM-based multi-agent systems are increasingly deployed on long-horizon tasks, but a single decisive error is often accepted by downstream agents and cascades into trajectory-level…
A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models
Zeru Shi, Zhenting Wang, Fan Yang +2
We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the \textbf{Massive Emergence Layer (ME Layer)}, that is consi…
Read the Scene, Not the Script: Outcome-Aware Safety for LLMs
Rui Wu, Yihao Quan, Zeru Shi +3
Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs that contain sensitive surface si…
Auto-Prompt Generation is Not Robust: Prompt Optimization Driven by Pseudo Gradient
Zeru Shi, Zhenting Wang, Yongye Su +5
While automatic prompt generation methods have recently received significant attention, their robustness remains poorly understood. In this paper, we introduce PertBench, a compreh…