Abstract
This disclosure presents Anchored AI, a system and method for ensuring reliable, human-aligned artificial intelligence through the integration of anchored data partitioning, intent extraction, anchor-weighted generation, provenance embedding, and synthetic collapse prevention. Unlike conventional large language models that are trained indiscriminately on mixed or synthetic data, Anchored AI introduces a structured framework where datasets are partitioned and weighted (anchor, ephemeral, synthetic), user queries are processed through an intent extraction layer (embedding, semantic role labeling, ontology mapping), and responses are generated with explicit anchor weighting and citation from authoritative corpora. Outputs are embedded with cryptographic provenance manifests (e.g., C2PA/PROV) to ensure auditability and compliance with emerging AI governance standards. A synthetic collapse guard enforces training hygiene by detecting AI-generated text, applying watermarking/metadata checks, and maintaining a human-data anchor to prevent recursive distributional drift. This combination addresses critical challenges in AI safety, reliability, and trustworthiness by making models both provably grounded and resistant to degradation.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Walker, Joseph JM, "Anchored AI: Intent-Extraction, Anchor-Weighted Generation, and Synthetic Collapse Prevention for Human-Aligned AI", Technical Disclosure Commons, (October 03, 2025)
https://www.tdcommons.org/dpubs_series/8677