Most of us use AI in a “conversation” mode. We ask a question, get an answer, refine it, until after 10 iterations we realize the model has “drifted” — it has lost the original assumptions, started agreeing with us (sycophancy), or simply produced elegant-sounding nonsense. If you are building complex knowledge systems, writing code, or doing research, you need to abandon the “chat” paradigm in favor of a State-Control Architecture. Introducing CMFL 2.5 (Cross-Model Feedback Loop) — a system where text is only a “side effect” of a stable logical graph. The Problem: Why “Self-Correction” Doesn’t Work Research (e.g., the Accuracy–Correction Paradox) shows that the strongest models (GPT, Gemini, etc.) paradoxically perform worse at correcting their own errors. Once a model “believes” its hallucination, it will defend it within the context window. The solution? Enforced heterogeneity. You must couple two different models (e.g., GPT and Gemini) in a loop where one builds and the other tries, at all costs, to break its logic. CMFL 2.5: Text as a Graph, Not a String In CMFL 2.5, we don’t “write an essay.” We build a Graph of Assertions. Each piece of knowledge is a node with an ID, a type (assumption, proof, conclusion), and a confidence level. How does it work in practice? Instead of copying entire blocks of text between chat windows, we pass only semantic diffs. Model A (Generator / GPT): Proposes the graph structure. Model B (Adversarial Auditor / Gemini): Is forbidden to agree. It searches for gaps, contradictions, and missing evidence. It returns only a list of patches. Model C (Validator): An independent arbiter that checks whether the new version has lost any facts from the previous one. 3. Three Pillars of a Stable System A. Relation Algebra Instead of Intuition Nodes in the graph are connected by strict relations (e.g., “Conclusion X follows from Assumption Y”). If the Auditor challenges Assumption Y, the system automatically flags Conclusion X for revalidation. This eliminates situations where a fix in one paragraph breaks logic elsewhere. B. Objective Function (Loss Function) The system does not aim for text that “sounds good.” It optimizes specific parameters: Inconsistency Score: The fewer contradictions, the better. Information Density: Maximum facts, minimum fluff. Information Gain: The system is rewarded for discovering new correlations, not just safely rewriting what is already known. C. Semantic Git (Versioning) Each iteration is a “commit.” If the system starts oscillating (fixing the same thing repeatedly), we perform a rollback to the last stable graph version and change the strategy (e.g., increase model temperature or switch to a more aggressive role). Why This Changes the Game When we treat text as a “projection of a knowledge graph,” three things happen: End of hallucinations: Every sentence must have a “parent” in the proof graph. Context efficiency: By sending diffs (differences), we avoid clogging the model’s memory with redundant repetitions. Determinism: The result does not depend on the model’s “mood,” but on a rigorous validation process. 5. How to Implement It (Even Manually) You don’t need an API to start. Open two windows (GPT and Gemini) and apply a Differential Protocol: Ask GPT for a list of numbered assertions. Paste the list into Gemini with the instruction: “Identify errors in the relationships between points. Do not rewrite the text; provide only a list of corrections.” Paste Gemini’s corrections back into GPT: “Integrate these remarks while preserving logical structure.” Conclusion Stop treating AI as a smart colleague you chat with. Start treating it as a knowledge compiler. CMFL 2.5 is the shift from AI Writing to Knowledge Engineering. submitted by /u/TeachingNo4435
Originally posted by u/TeachingNo4435 on r/ArtificialInteligence