collaborators

7 papers

cs.AI2026

Who Speaks, Who Authorizes, Who Acts? Understanding Trust Boundaries in Multi-Agent LLMs

Junru Zhu, Xin Chen, Hang Lyu +6

Multi-agent LLM systems exchange messages before acting through tools, but message delivery, signing authority, and tool-side enforcement are separate trust boundaries. We represen…

cs.CR2026

RAGScope: A Leakage-Controlled, Cost-Aware Evidence-Gating Protocol for RAG Hallucination Triage

Zeming Liu, Qibai Chen, Jingtao Zhang +1

Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and reserve strong verifiers for the…

cs.AI2026

Knowing When to Yield: Grounded Arbitration of User Corrections in Text-Based Embodied Agents

Yezhou Cheng, Runjia Du, Zeming Liu +3

How should an embodied agent respond when a person's correction may be wrong? We formulate grounded correction arbitration as a choice among accepting, rejecting, inspecting the wo…

cs.AI2026

Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory

Yezhou Cheng, Runjia Du, Zeming Liu +5

Persistent memory lets language-model agents improve prompts and skills without updating model weights. We show that matching retrieval scope to certification scope enables these e…

cs.AI2026

Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation

Yezhou Cheng, Runjia Du, Zeming Liu +5

Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty. We characterize that requirement on a fixed grid of $…

cs.LG2026

LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels

Zeming Liu, Hang Lyu, Jingtao Zhang +1

Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment settings, however, labels com…

cs.SE2026

FaultLens: Learning Compact Behavioral Test Suites for Generated Operational Programs

Zeming Liu, Hang Lyu, Jingtao Zhang

Generated operational programs are often validated with either a few hand-written examples or exhaustive regression suites. The former can miss sparse boundary and interaction faul…