- 8properties bound into every index's attestation
- 4checks: build, persist, publish and load
- ≤ 1active generation per index at any moment
- 1 → 2one GPU read per member vector, two computations from it
Methods and Systems for a Self-Verifying Deterministic Vector Index and GPU Retrieval Substrate Materialized From a Relational System of Record. U.S. Provisional Patent Application No. 64/148,001 (patent pending). Embodied in the vector store of the Inference Fabric.
One authoritative source
Traditional vector pipelines create multiple copies of data, a source database, a vector store, index files, GPU memory, with no provable link between them. Data can drift, go stale or be corrupted, and nobody can prove what produced a result. Here the relational database is the only authority. Vectors are exported deterministically into memory-mapped shards that serve reads and carry references back to the database; they are serving representations, never another authoritative copy.
Deterministic construction
Index construction is deterministic: given the same inputs, parameters, seed, format and implementation, the index body is byte-identical. That makes index artifacts repeatable and checkable rather than one-off files.
Source-bound attestation
Every index is cryptographically bound to its source data and build conditions. A binding hash ties the full input identity, the build parameters, the format version and the index body together.
Refuse rather than trust
Artifacts are checked at build, at persist and at load time, and publishing is allowed only when what reached the disk matches what was built in memory. A mismatch is a refusal, never a warning.
Verified generations
All artifacts of a generation, centroids, members and derived data, are verified together before the generation is activated, so readers never see a torn set.
Journal-bound windows
Data windows are sealed with a content hash and a journal sequence, and consumers re-verify them before use.
One GPU read, two computations
The retrieval does two computations from one read: each member vector is read from GPU memory once and feeds both the similarity score and the cluster-health metrics.
Cluster health beside every result
The health metrics are the mean distance to the centroid, the mean squared distance, and two bounded health scores derived from them, so results carry both what is closest and how coherent the region was.
Lineage that survives partial failure
Results and health metrics carry the same lineage even when one component fails, so the relationship between a result and its health computation is never lost.
Residency keyed by generation
GPU memory tracks which data generations it holds and evicts only the artifacts a change affected, never the whole cache.
From an authoritative source through deterministic materialisation and attestable indexes to GPU retrieval with provenance, the same data gives the same result along a path that can be checked from source to answer.
Every boundary is identifiable, verifiable, or refused.