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
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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.
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.
citing papers explorer
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BiAxisAudit: A Novel Framework to Evaluate LLM Bias Across Prompt Sensitivity and Response-Layer Divergence
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.
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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.
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CommitDistill: A Lightweight Knowledge-Centric Memory Layer for Software Repositories
CommitDistill is a deterministic, local-only prototype that extracts typed knowledge from git commits and evaluates retrieval performance against baselines on public repositories.
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Adaptive and AI-Augmented Security Testing: A Systematic Survey of Program Analysis, Feedback-Driven Testing, and Hybrid Learning-Based Approaches
Systematic survey of 55 studies on security testing identifies structural-adaptive fragmentation between program representations and adaptive mechanisms, proposing a unified research agenda.