Retrieval Unreliability and Knowledge Base Corruption

TITLE OF THE INVENTION

Secure, Provenance‑Driven Retrieval‑Augmented Generation System with Adaptive Trust, Hybrid Retrieval, and Immutable Audit Trail

FIELD OF THE INVENTION

The present invention relates to artificial intelligence systems, specifically to retrieval‑augmented generation (RAG) architectures that incorporate cryptographic provenance, dynamic trust scoring, hybrid sparse‑dense‑graph retrieval, and tamper‑evident audit logging for secure, interpretable knowledge‑base usage.

BACKGROUND AND PRIOR ART

Current RAG pipelines suffer from fragmented defenses that operate at isolated stages such as retrieval, post‑retrieval clustering, or pre‑generation filtering, thereby lacking end‑to‑end provenance and accountability ^[1]. Existing vector stores expose embeddings as unprotected numeric arrays, enabling malicious injection or steganographic exfiltration ^[v4257]. While cryptographic signing of embeddings has been proposed to prevent tampering ^[5], it is rarely integrated with dynamic trust weighting or hybrid retrieval. Hybrid sparse‑dense engines improve recall but can still be dominated by poisoned passages ^[6]. Immutable audit trails using blockchain technology have been demonstrated for secure logging ^[v7283], yet they are not coupled to RAG workflows. Consequently, a comprehensive, provenance‑driven RAG architecture that simultaneously mitigates membership inference, data poisoning, and content leakage while providing traceability and rollback remains unsolved.

SUMMARY OF THE INVENTION

The invention provides a holistic RAG architecture that interweaves cryptographically signed vector ingestion, adaptive trust‑weighted retrieval, a hybrid sparse‑dense‑graph engine, immutable audit‑trail logging with rollback capability, self‑critiquing generation, and adaptive knowledge‑base versioning. This integrated system delivers end‑to‑end provenance, mitigates multi‑vector attacks, preserves semantic utility, and enables automated rollback upon corruption detection.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiment 1 – Cryptographically Signed Vector Ingestion
Each embedding is generated from a source document and accompanied by a hash of the document content, the encoding model version, and a timestamp. The hash is signed by a trusted ingestion service, such as a blockchain oracle ^[5], and stored alongside the vector in the vector database. During retrieval, the system verifies the signature to confirm that the vector originates from an unaltered, authorized source, preventing silent poisoning ^[v2168] and ^[v4257].

Embodiment 2 – Dynamic Trust‑Weighted Retrieval
A trust score \(T_i\) is assigned to each vector based on provenance metadata, historical query success, and peer‑reviewed annotations. Retrieval queries rank candidates by a composite metric \(\alpha \cdot \text{similarity} + (1-\alpha)\cdot T_i\), where \(\alpha\) adapts to the confidence of the query context. This mechanism mitigates membership inference by dampening the influence of overly popular vectors and reduces poisoning by down‑weighting suspect vectors ^[1] and ^[v14295].

Embodiment 3 – Hybrid Sparse‑Dense‑Graph Retrieval Engine
The engine first performs dense semantic scoring to capture recall, then re‑ranks candidates using a sparse lexical index to preserve exactness for identifiers and policy strings ^[6] and ^[v1372]. A lightweight graph layer encodes relationships such as entity co‑occurrence and policy dependencies, enabling multi‑hop reasoning. Retrieval proceeds in stages: dense scoring → sparse re‑ranking → graph consistency checks, thereby reducing the risk that a single poisoned passage dominates the context.

Embodiment 4 – Audit‑Trail & Rollback Layer
Every retrieval, inference, and subsequent action is logged with a retrieval trace that records vector IDs, similarity scores, and trust weights. The trace is stored immutably in a tamper‑evident ledger, such as a permissioned blockchain ^[5], ^[v7283], and ^[v9717]. Upon detection of corruption, the system automatically rolls back to a previous consistent state and flags offending vectors for deprecation.

Embodiment 5 – Self‑Critiquing Retrieval‑Augmented Generation
The LLM is augmented with a critic module that evaluates the faithfulness of each generated statement against the retrieved evidence, inspired by the GRAG critic module ^[7] and validated in the DocSync framework ^[v16044]. If the critic detects low overlap or contradictory evidence, it triggers a re‑retrieval, enforcing a continuous correctness loop.

Embodiment 6 – Adaptive Knowledge‑Base Versioning
Embeddings are tagged with a semantic version reflecting the model and corpus state. When underlying models evolve, the system re‑indexes affected vectors in a shadow index and verifies consistency before promoting them to the production index, preventing semantic drift ^[4] and ^[v7408].

CLAIMS

1. A method for secure retrieval‑augmented generation comprising: cryptographically signing each embedding with a hash of the source document, encoding model version, and timestamp, and storing the signature in a tamper‑evident ledger; assigning a dynamic trust score to each vector based on provenance metadata and historical query success; ranking retrieval candidates by a composite metric of similarity and trust score; performing hybrid retrieval that first applies dense semantic scoring, then sparse lexical re‑ranking, and finally graph consistency checks; logging every retrieval and inference step with vector identifiers, similarity scores, and trust weights in an immutable audit trail; triggering a critic module to evaluate faithfulness of generated content against retrieved evidence and re‑retrieving if necessary; and maintaining adaptive knowledge‑base versioning by re‑indexing embeddings in a shadow index before promotion to production.

2. The method of claim 1, wherein the cryptographic signing is performed by a blockchain oracle that issues a digital signature over the hash of the source document.

3. The method of claim 1, wherein the trust score is computed as a weighted sum of provenance confidence, historical query success rate, and peer‑reviewed annotation quality.

4. The method of claim 1, wherein the composite ranking metric is \(\alpha \cdot \text{similarity} + (1-\alpha)\cdot T_i\) with \(\alpha\) adaptively set based on query context confidence.

5. The method of claim 1, wherein the hybrid retrieval engine first performs dense vector similarity search, then applies a sparse BM25 re‑ranking, and finally executes graph traversal to enforce consistency constraints.

6. The method of claim 1, wherein the immutable audit trail is stored in a permissioned blockchain that records retrieval traces, similarity scores, trust weights, and timestamps.

7. The method of claim 1, wherein the critic module evaluates faithfulness by computing overlap between generated statements and retrieved evidence and re‑retrieves if overlap falls below a threshold.

8. The method of claim 1, wherein adaptive knowledge‑base versioning tags embeddings with a semantic version and promotes vectors from a shadow index to production only after consistency verification.

9. A system for secure retrieval‑augmented generation comprising: a cryptographic ingestion module that signs embeddings; a trust‑scoring module that assigns dynamic trust scores; a hybrid retrieval engine that integrates dense, sparse, and graph retrieval; an immutable audit‑trail module that logs retrieval and inference events; a critic module that evaluates faithfulness and triggers re‑retrieval; and a versioning module that manages adaptive knowledge‑base updates.

10. The system of claim 9, wherein the cryptographic ingestion module uses a blockchain oracle to sign embedding hashes.

11. The system of claim 9, wherein the trust‑scoring module computes scores based on provenance metadata, historical query success, and peer‑reviewed annotations.

12. The system of claim 9, wherein the hybrid retrieval engine performs dense scoring, sparse re‑ranking, and graph consistency checks in sequence.

13. The system of claim 9, wherein the immutable audit‑trail module stores logs in a tamper‑evident ledger and supports automatic rollback to a prior consistent state upon corruption detection.

14. The system of claim 9, wherein the critic module evaluates faithfulness of generated content against retrieved evidence and initiates re‑retrieval when necessary.

15. The system of claim 9, wherein the versioning module tags embeddings with semantic versions and promotes vectors from a shadow index to production only after consistency verification.

ABSTRACT

A secure retrieval‑augmented generation architecture is disclosed that integrates cryptographically signed vector ingestion, dynamic trust‑weighted retrieval, hybrid sparse‑dense‑graph search, immutable audit‑trail logging with rollback capability, self‑critiquing generation, and adaptive knowledge‑base versioning. Embeddings are signed by a trusted oracle and stored with provenance metadata; each vector receives a trust score derived from provenance and historical performance, and retrieval candidates are ranked by a composite similarity‑trust metric. Retrieval proceeds through dense semantic scoring, sparse lexical re‑ranking, and graph consistency checks, ensuring resilience against membership inference, data poisoning, and content leakage. All retrieval and inference events are logged immutably in a blockchain ledger, enabling automated rollback upon corruption detection. A critic module evaluates faithfulness of generated content and triggers re‑retrieval when necessary, while embeddings are versioned and promoted only after consistency verification, thereby preserving semantic utility and providing end‑to‑end provenance and interpretability for multi‑agent AI systems.