collaborators

22 papers

cs.AI2026

Learning-Driven Adaptive Audit Scheduling: A Sequential Decision Approach to Off-Chain Data Integrity

Changting Lin, Fan Li, Weihang Yu +4

We model cryptographic auditing of off-chain data as a Constrained MDP (CMDP) under partial observability: the storage node's hidden type and corruption state make the problem a PO…

cs.AI2026

FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG

Zhe Yu, Wenpeng Xing, Tiancheng Zhao +3

When retrieved evidence contradicts parametric memory, language models frequently ignore context and default to memorized priors -- a failure that undermines the core purpose of re…

cs.AI2026

Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs

Zhe Yu, Wenpeng Xing, Chen Ye +4

Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness whe…

cs.AI2026

Silencing the Guardrails: Inference-Time Jailbreaking via Dynamic Contextual Representation Ablation

Wenpeng Xing, Moran Fang, Guangtai Wang +2

While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak attacks that circumvent safety constraints. Existing strategies, rangin…

cs.LG2026

MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE

Zixuan Chen, Heng Zhang, YuPeng Qin +5

Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heterogeneous omics and clinical data.…

cs.CR2026

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends

Zhenhua Xu, Xubin Yue, Zhebo Wang +9

Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys h…