Permit identifies a permission-sensitive subspace in LLM hidden states and applies lightweight offset or gated interventions to enforce fine-grained generation control, outperforming prior methods with over 18% F1 gain and near-zero leakage using over 98% fewer parameters.
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A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.
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Permit: Permission-Aware Representation Intervention for Controlled Generation in Large Language Models
Permit identifies a permission-sensitive subspace in LLM hidden states and applies lightweight offset or gated interventions to enforce fine-grained generation control, outperforming prior methods with over 18% F1 gain and near-zero leakage using over 98% fewer parameters.
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Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework
A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.