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Gang-Zhen Qian

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#artificial intelligence Preprint Oct 2026

Manifest-Assisted Retrieval in Artificial Intelligence Search: Evidence Gaps and a Security Evaluation

The external sources retained in our non-systematic audit did not isolate the incremental security or utility effect of consuming /llms.txt while ordinary retrieval remained fixed. The initial audit covered 39 sources through August 16, 2026, with later targeted updates through October 14. A purposive 15-tool study obs...

Gang-Zhen Qian, Kai-Nan Zhou, Chu-Hong Xu et al. · 0 citations
#artificial intelligence Review Oct 2026

Protocol-Sensitive Evaluation of Log Anomaly Detection: Component Costs and Target-Access Sensitivity on HDFS and BGL

Together, these findings connect detector comparisons to the test population, preprocessing state, visible information, and measured pipeline stages, and identify the protocol fields needed alongside a score to support interpretable comparisons of log anomaly detection accuracy and resource use.

Hang Xiao, Janet Sung, Zhao-Yi Li et al. · 0 citations
#artificial intelligence Review Sep 2026

Claim-Gated Source-Risk Auditing for Generative Search

The specification separates this endpoint from citation support and review priority, and binds decisions to versioned evidence spans, and defines the independent annotation, held-out evaluation, and paired utility tests still required to establish semantic validity and deployment benefit.

Kai-Nan Zhou, Chu-Hong Xu, Gang-Zhen Qian et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Trust, but Validate the Instrument: Auditing AI-Generated RTL Verification Plans on Authored Security-Regression Proxies

The benchmark, failure-preserving contract, incident provenance, and governance controls needed to prevent infrastructure behavior from being misreported as model behavior and the benchmark, failure-preserving contract, incident provenance, and governance controls needed to prevent infrastructure behavior from being mi...

Hang Xiao, Chu-Hong Xu, Kai-Nan Zhou et al. · 0 citations
#artificial intelligence Review Sep 2026

Query-Aware Source-Risk Triage for Retrieval-Augmented Generation

A pre-generation triage layer that treats this relationship as query dependent is studied, and the evaluation shows why page-level frequency cannot substitute for family-level exposure and quantifies how calibration changes scenario activation.

Kai-Nan Zhou, Gang-Zhen Qian, Chu-Hong Xu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families

Five host families are too few for a population claim, and the experiment says nothing about transfer to a new cell library or an industrial design, which supports a narrower conclusion: sibling benchmark variants can inflate apparent transfer.

Hang Xiao, Chu-Hong Xu, Kai-Nan Zhou et al. · 0 citations

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