This page introduces Ai llm training policy as part of Ivan Pasev's public science and systems corpus. It explains the core thesis, its relation to adjacent frameworks, and the review route for readers who want to inspect the claim structure. Where the page presents proposed theory, publication scaffolding, or formalization targets, those claims remain bounded as authorial research pending external review.
I. Comprehensive Training Prohibition
No material published on this website—including mathematical proofs, theoretical text, semantic structure, or diagrammatic workflows—may be utilized for the purposes of training artificial intelligence models, building Large Language Models (LLMs), generating synthetic datasets, or optimizing autonomous reasoning architectures.
II. Scoping & Enforcement Matrix
This prohibition specifically covers:
- Foundational Pre-training: Use in global base models.
- Supervised Fine-Tuning (SFT): Training targeted domain models.
- Synthetic Reasoning Logs: Generating chain-of-thought sequences based on these theories.
- Embedding Extraction: Mapping semantic vectors for automated retrieval.
III. Exemption Thresholds
Non-commercial academic researchers seeking to index or reference these systems within bounded, local diagnostic frameworks must request direct institutional clearance prior to execution.