Abstract
A system, method, and middleware protocol provide deterministic financial reconciliation for assertions generated by non-deterministic generative artificial intelligence models. An inference interception layer captures hidden state activations and logit probability distributions during token generation prior to natural language decoding. An assertion extraction and classification module identifies hard factual assertions, decomposing them into semantic triplets. An entropy-to-escrow mapping engine calculates a fidelity quotient based on normalized semantic entropy, attentional saliency across neural attention heads, and historical participant reliability. When semantic entropy exceeds a threshold, a generative assertion bond is calculated and locked in zero-knowledge escrow via a smart contract. A decentralized truth oracle evaluates assertions against canonical manifests using cosine similarity and semantic embedding distance. If a semantic mismatch is detected, an automated slashing event transfers the bond to the counterparty. An adversarial input neutralization module scans prompt geometry for instructional pressure to prevent induced hallucination fraud. Cross-lingual semantic alignment projects assertions into an interlingual vector space to prevent false slashing from linguistic drift. Multi-agent cascading escrow nests sub-bonds across agent chains to assign liability recursively.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
N/A, "Dynamic Semantic Fidelity Protocol: System and Method for Deterministic Financial Reconciliation of Generative Artificial Intelligence Assertions Via Internal Attentional State Mapping and Programmatic Collateralization", Technical Disclosure Commons, (October 20, 2026)
https://www.tdcommons.org/dpubs_series/12334