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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 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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