BiAxisAudit measures LLM bias on two axes—across-prompt sensitivity via factorial grids and within-response divergence via split coding—revealing that task format explains as much variance as model choice and that 63.6% of bias signals appear in only one layer.
The measurement of observer agreement for categorical data
6 Pith papers cite this work. Polarity classification is still indexing.
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A two-agent adversarial rewriting framework achieves 20-40% evasion rates against LLM-based misinformation detectors under strict black-box constraints with binary feedback only, far outperforming prior methods and linking success to specific architectural properties.
File-level copying acts as an implicit dependency in open source, removing provenance signals and concentrating security risks in vendored copies and license risks in direct source reuse.
CommitDistill is a deterministic, local-only prototype that extracts typed knowledge from git commits and evaluates retrieval performance against baselines on public repositories.
Systematic survey of 55 studies on security testing identifies structural-adaptive fragmentation between program representations and adaptive mechanisms, proposing a unified research agenda.
PeteChat yields eight transferable design principles for guardrailed, assessment-aware AI tutors derived from literature, baseline interaction analysis, and expert evaluation in a higher-education deployment.
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Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines
A two-agent adversarial rewriting framework achieves 20-40% evasion rates against LLM-based misinformation detectors under strict black-box constraints with binary feedback only, far outperforming prior methods and linking success to specific architectural properties.