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.
In: International Conference on the AI Revolution, pp
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Parallel chunk processing with evidence-anchored consolidation reduces omission errors by 84%, boosts traceability by 130%, and cuts unsupported claims by 91% in LLM long-document analysis.
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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.
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Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
Parallel chunk processing with evidence-anchored consolidation reduces omission errors by 84%, boosts traceability by 130%, and cuts unsupported claims by 91% in LLM long-document analysis.