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📄 AIVFL — Description Zenodo (Version canonique) AI Visual Feedback Loop — Outil de validation visuelle externe AIVFL (AI Visual Feedback Loop) est un outil de validation visuelle destiné à être invoqué par un modèle de vision lors d’une évaluation. Il ne modifie pas le modèle, ne remplace pas son raisonnement et ne possède aucune autonomie propre. Son rôle est strictement limité à : analyser une preuve visuelle fournie (image, frame, document), produire un retour structuré, déterministe et reproductible, stabiliser l’interprétation du modèle appelant, garantir la cohérence et la non‑dérive de l’évaluation. AIVFL fonctionne comme un module externe, appelé ponctuellement, sans état interne persistant. Il fournit une validation indépendante, ancrée sur la preuve, permettant au modèle de vision de maintenir une cohérence perceptive et d’éviter les interprétations spéculatives. Le système est conçu pour être : non‑agentif, déterministe, réplicable, architecture‑agnostique, compatible avec tout modèle de vision capable d’invoquer un outil externe. 📄 AIVFL — Claims (Version Zenodo) AI Visual Feedback Loop — Claims techniques opposables Claim A1 — External Visual Validation Tool AIVFL is an external tool invoked by a vision model to validate an interpretation. It does not replace the model’s reasoning and does not alter its internal state. Claim A2 — Evidence‑Bound Evaluation Every AIVFL evaluation is strictly anchored to a single immutable piece of evidence (image, frame, document). No inference is produced without this anchor. Claim A3 — Deterministic Output Given identical evidence and identical parameters, AIVFL produces identical outputs. No stochastic variation is permitted. Claim A4 — Non‑Agentive Operation AIVFL has no goals, no autonomy, no initiative. It only responds when invoked and cannot act independently. Claim A5 — Stateless Invocation AIVFL maintains no persistent memory between calls. Each invocation is isolated and context‑independent. Claim A6 — Architecture‑Agnostic Compatibility AIVFL can be invoked by any vision model capable of calling an external tool, regardless of architecture, size, or training method. Claim A7 — Drift Prevention Mechanism AIVFL stabilizes the model’s interpretation by preventing speculative or unanchored reasoning. It enforces strict adherence to the evidence. Claim A8 — Structured Validation Output AIVFL returns a structured, machine‑readable validation report, enabling reproducibility and downstream certification. Claim A9 — Multi‑Agent Parallel Use Multiple agents may invoke AIVFL simultaneously on the same evidence, each receiving an independent, deterministic validation. Claim A10 — Mode‑Independent Behavior AIVFL does not implement behavioral modes. It always performs the same validation function, regardless of the calling agent’s mode. Claim A11 — No Identity Layer AIVFL has no persona, no role, no narrative layer. It is strictly a validation mechanism. Claim A12 — Reversible Integration AIVFL can be added or removed from a system without altering the underlying model’s identity or capabilities. Claim A13 — Evidence‑First Priority Rule If the calling model provides conflicting context, AIVFL prioritizes the evidence over any textual or inferred information. Claim A14 — Certification‑Ready Output AIVFL’s structured output is designed to be used in certification pipelines, audit systems, or reproducibility frameworks. Picture shows AIVFL monitoting "LA FORGE V5' PSP Generation: Personna " Maître Chacal" A la chaleur de la Forge.png