Abstract
Causal-Trace Embedding Graphs (CTEG) introduce a lineage-aware semantic representation framework in which embeddings are no longer treated as isolated vectors but as nodes within a dynamically evolving graph that encodes the full causal history of their formation. Each embedding is augmented with structured provenance, transformation metadata, and dependency relationships that together form a directed acyclic or cyclic graph capturing how information has been derived, transformed, and propagated through an AI system. This architecture enables retrieval and reasoning processes to incorporate not only semantic similarity but also trace consistency, transformation reliability, and lineage integrity. By embedding causal structure directly into the representation layer, CTEG addresses fundamental limitations in current systems related to context misalignment, unverifiable outputs, and lack of governance over data transformations. The disclosed system provides a mathematically and computationally grounded pathway toward explainable, auditable, and self-consistent semantic infrastructures suitable for large-scale deployment in high-assurance environments.
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Recommended Citation
Manuel-Devadoss, Johny, "Causal-Trace Embedding Graphs (CTEG)", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9959