most citedA Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures

2 citations · 3 across the 12 of their papers we have counts for

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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.AI2026

HGMF: A Hierarchical Gaussian Mixture Framework for Scalable Tool Invocation within the Model Context Protocol

Wenpeng Xing, Zhipeng Chen, Changting Lin +1

Invoking external tools enables Large Language Models (LLMs) to perform complex, real-world tasks, yet selecting the correct tool from large, hierarchically-structured libraries re…