AI Agents and LLM Systems
Research-grounded analysis of AI agents and LLM systems: architecture, security, memory, RAG, MCP, prompt engineering, model behavior, and evaluation.
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Cognitive Closure in AI Use: Why We May Stop Too SoonHow the need for cognitive closure may interrupt the evaluation, feedback, and revision needed to improve an AI-gen...
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How to Write for AI Models: The Language of Effective PromptsLearn how to replace conversational filler, theatrical personas, leading premises, and competing tasks with prompt ...
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Role-Play Jailbreaks: A Testable False-Premise MechanismA research-grounded analysis of how role-play may introduce a false safety premise that changes an LLM's interpreta...
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Can AI Lie? The Difference Between Error, Hallucination, and DeceptionA false AI answer is not automatically a lie. This article separates human lying, model confabulation, unsupported ...
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Model Capability vs. System Capability: 26 Technical BridgesA technical map of how memory, retrieval, multimodal models, planners, tools, solvers, verifiers, and feedback loop...
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Human Capabilities vs. Human-Like AI Behavior: A Mechanism-Based MapA research-grounded comparison of 26 human capabilities across cognitive processes, neurobiological and bodily impl...