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Project Eidolon

Project Eidolon explains how LLMs work by examining how and why they fail.

It is a systematic study of "hallucination" not as a bug, but as an architectural feature of coherence-optimized systems. It provides a standardized terminology stack, technical analysis of failure modes in agentic workflows, and a framework for understanding "confidence theater."

What Problem This Addresses

  • The Terminology Gap: "Hallucination" is anthropomorphic; "Error" is too vague. We lack precise language for why a model invents a fact.
  • The Agentic Risk: As LLMs move into IDEs and autonomous roles, "plausible but ungrounded" outputs (confabulations) become critical system risks.
  • The Confidence Illusion: Users mistake stylistic fluency for epistemic certainty.

Who This Is For

  • Developers & Architects: Building agentic systems (IDE agents, MCP servers, RAG pipelines).
  • Researchers: Looking for a comparative analysis of model failure modes.
  • Educators: seeking to explain LLM limitations without mysticism.

What This Is Not

  • Not about Sentience: We do not discuss "thinking" or "feeling."
  • Not about "AI Lying": There is no intent to deceive, only an optimization for pattern completion.
  • Not a Prompt Library: This is an architectural analysis, not a collection of "jailbreaks."

Navigation

  • EIDOLON_PRIMER.md: The core thesis and terminology stack. Start here.
  • /docs/: Detailed analysis of mechanisms, failure modes, and mitigation.

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A systematic study of LLM "hallucination" as an architectural feature of coherence-optimized systems. Standardized terminology, failure mode analysis, and mitigation strategies.

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