Every sufficiently coherent system contains implied gaps — places where the internal logic points toward content that was never written, lost to history, or not yet derived.

KEGA (Knowledge Extension via Gap Analysis) proposes that these gaps are not missing — they are constrained. The surrounding structure limits what the missing content can logically be. In many cases, the gap is recoverable not through new empirical data, but through systematic reasoning from existing structure.

Three Operational Modes
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Science Mode — coherent formal systems with identifiable structural gaps. Output is verifiable. The derived content can be tested or proven.

Art Mode — narrative or creative systems. Output is plausible, constrained by the system but not uniquely determined. Think: generating the 10th chapter of a 9-chapter novel.

Experiment Generation Mode — the middle layer. Given a theory, derive the constrained set of experiments that would verify or falsify it. Creative but falsifiable.

Validated On
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  • Experiment 1: Gap analysis on Goodfellow et al.’s Deep Learning — deriving diffusion model theory from structural gaps in the mathematical derivation chain.
  • Experiment 2: Gap analysis on the Guiguzi (鬼谷子, ~475 BCE) — reconstructing two historically lost chapters and deriving four implied extensions from the surviving twelve-chapter framework.

Two independent domains. Same method. Both worked.

The Core Implication
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KEGA converts discovery problems into verification problems.

Discovery: What is the missing piece? — open-ended, expensive.
Verification: Which of these constrained candidates is correct? — bounded, tractable.

Verification is almost always cheaper than discovery. KEGA compresses research timelines not by eliminating work, but by changing its type.

Where This Goes Next
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A few directions the framework naturally points toward:

  • Worked example: Given the Zuck-Chai Equation, what experiments does KEGA imply? Which conditions would verify it, which would falsify it, and which edge cases does the equation not yet address? That’s Experiment Generation Mode in action.
  • Agentic pipeline: KEGA + RAG + LLM as a research acceleration loop — ingest a canonical text, surface structural gaps automatically, generate constrained candidates, return a ranked verification agenda.
  • Stress-test the coherence threshold: How much structure is “enough” for KEGA to work? Finding the minimum coherence floor is the next theoretical question.

The methodology is open. Apply it, break it, extend it.

Draft Paper
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The full paper is available below. The human conclusion section is intentionally left blank — for the author, or for the disciple who earned it.

Developed through human-AI collaboration, March 2026.