The first anti-AI tarpit. Rewritten in Python. Traps LLM crawlers in an infinite maze of fake pages and Markov babble.
No system can model its own source. Empirical proof: 6 AI architectures (GPT-4, Claude, Gemini, DeepSeek, Grok, Mistral) hit the same structural wall.
The mathematical proof of AI Model Collapse via Semantic Contraction.
Human Signal Intelligence Protocol. Behavioral data sovereignty on TON blockchain. TEE + Proof-of-Behavior + $FORE token. Fair Launch Q3 2026.
Multi-generation LLM distillation experiment studying convergent collapse across iterative self-training loops
Conceptual Latent Engineering (CLE) Framework Codex Version 1.0 — Grounding high-dimensional vector space within objective reality constraints.
GenProof detects model collapse risk in pre-training datasets before training begins. It combines semantic entropy, tail-density, and AI detection into a composite probability score (ICS). Built with FastAPI and scikit-learn to help ensure data quality and compliance.
Project on Model Collapse in LLMs – Big Data Engineering (MSc), supervised by Prof. V. Moscato, PhD G. M. Orlando and PhD D. Russo (2025)
OEA: Structured Recursive Calibration for Generative Stability — empirical study of directional calibration and epistemic filtering in recursive LLM generation. doi:10.5281/zenodo.20412150
Psychohistory Prediction Engine — 138-year cycle analysis, structural pressure scoring, and falsifiable predictions for 2026-2040
Admissibility and parallax instability detection for AI systems
Code, phase portraits, and dynamic simulations for the preprint "CREATIVE LOOP: Informational Replication Dynamics and Stability
TaQuants protects you from model collapse.
Synthetic corpus contamination & token entropy auditing for LLM pre-training data — find AI-generated text in a corpus before it poisons your tokenizer. Zero dependencies.
A draft algorithmic specification for estimating origin purity, AI-generated ratio, warning flags, and review readiness in AI source-preservation systems.
🌙 Setzt die 'Gedanken' einer KI nächtlich auf einen geprüften Stand zurück – das Können bleibt, die Ausartung (Model Drift/Collapse) wird verworfen. Python, 0 Dependencies.
Description: A draft v0.2 specification for AI origin-purity scoring, warning-flag severity, recursive synthetic risk detection, and review routing.
🛡️ Framework de défense contre le Vandalisme Cognitif et l'empoisonnement de données dans les LLMs. Analyse quantitative du révisionnisme historique, métriques de dérive morale et implémentation de preuves de réalité par hachage temporel (C2PA/Blockchain)
Early Detection of Model Collapse in AI Systems using Statistical and Semantic Analysis
Experiments for my Bachelor's thesis on fine-tuning language models and analyzing model collapse on synthetic generational data.
To associate your repository with the model-collapse topic, visit your repo's landing page and select "manage topics."