A computer-implemented method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data, the method executed by a computing system comprising a data ingestion unit, a blockchain anchoring unit, a cross-domain harmonization processor, a foundation model processor, a verification processor, and a governance processor, the method comprising:
- receiving, by the data ingestion unit, a plurality of heterogeneous datasets originating from multiple independent domains including at least a financial domain, a healthcare domain, and an environmental domain, wherein each dataset is received in a native encoding format and is transformed through schema normalization and temporal alignment into tensorized machine-readable feature representations;
- computing, by the blockchain anchoring unit, a cryptographic hash value corresponding to each dataset instance using a secure hashing function and embedding said hash along with metadata including a dataset identifier, acquisition timestamp, and contributor credential into a distributed ledger maintained by a plurality of blockchain nodes;
- transforming, by the cross-domain harmonization processor, the blockchain-anchored dataset records into a unified latent representation by executing a multi-stage transformer architecture that performs intra-domain feature encoding followed by inter-domain alignment, wherein alignment is achieved by minimizing divergence across feature probability distributions derived from said domains;
- invoking, by the foundation model processor, a Bedrock-based foundation model fine-tuned with domain adapter layers to receive the unified latent representation and to compute predictive inference outputs through attention-based contextual propagation across said latent representations;
- validating, by the verification processor, said predictive inference outputs by cross-referencing the inference lineage against blockchain-anchored ground truth records, and
- determining an inference deviation value by calculating the absolute difference between the predicted tensor and a consensus-based reference tensor derived from said ground truth records;
- and
- recording, by the governance processor, the predictive inference outputs, the computed inference deviation value, and a cryptographic fingerprint of the updated model state into the distributed ledger, wherein transforming the blockchain-anchored dataset records into the unified latent representation further comprises dynamically recalibrating inter-domain alignment weights by computing, for each incoming dataset instance, a domain influence coefficient generated through temporal stability analysis of historical feature distributions, and further wherein the cross-domain harmonization processor applies a stabilization encoder to feature subsets associated with below-threshold influence coefficients, the stabilization encoder performing weighted singular value compression followed by reconstruction of the feature subset such that volatility in the latent representation is reduced prior to being incorporated into the unified latent encoding stream, and wherein invoking the Bedrock-based foundation model further comprises executing a hierarchical attention correction operation that detects semantic drift within the unified latent representation by comparing early-layer attention maps with corresponding deep-layer attention projections, and wherein upon detecting such drift, the foundation model processor recomputes contextual embeddings for affected latent segments using an adaptive normalization threshold applied to query-key keypoint alignments, and reinserts the corrected embeddings into the inference pipeline using a temporally ordered context merge routine that preserves causal dependencies across latent segments during predictive propagation.