Corpora.ai today announced a new end‑to‑end retrieval‑augmented generation (RAG) framework that guarantees the integrity of every piece of information fed to autonomous agents. By embedding cryptographic signatures, adaptive trust weighting, and immutable audit trails into the core of the pipeline, the system blocks poisoning, membership inference, and content leakage before they can influence model outputs. The solution also includes a lightweight critic that continuously verifies faithfulness, triggering re‑retrieval when evidence diverges. Together, these innovations create a self‑healing, auditable knowledge base that meets the highest standards of security and interpretability.
At the heart of Corpora.ai’s approach is cryptographically signed vector ingestion. Each embedding carries a hash of the source document, the encoding model version, and a timestamp, signed by a trusted ingestion service such as a blockchain oracle. This guarantees that any vector retrieved has not been tampered with, preventing silent poisoning attacks that have plagued traditional RAG pipelines.
Dynamic trust‑weighted retrieval further protects against adversarial manipulation. Every vector is assigned a trust score derived from provenance metadata, historical query success, and peer‑reviewed annotations. Retrieval queries rank candidates by a composite metric that blends semantic similarity with trust, automatically dampening the influence of overly popular or suspect vectors and thereby mitigating membership inference and poisoning.
The hybrid sparse‑dense‑graph engine combines dense semantic recall with sparse lexical precision and a lightweight graph layer that encodes entity co‑occurrence and policy dependencies. Retrieval proceeds in stages—dense scoring, sparse re‑ranking, and graph consistency checks—ensuring that a single poisoned passage cannot dominate the context. This layered approach delivers higher recall and precision while keeping latency low.
Every step of the process is logged in an immutable, tamper‑evident ledger. Retrieval traces, similarity scores, and trust weights are recorded on a permissioned blockchain, enabling automatic rollback to a previous consistent state when corruption is detected. Coupled with a self‑critiquing generation module that verifies faithfulness against retrieved evidence, the system closes a continuous correctness loop that eliminates hallucinations and guarantees auditability.
Key Facts
- Cryptographic signatures on every embedding prevent silent poisoning and enable end‑to‑end provenance.
- Dynamic trust weighting reduces hallucinations by 30‑40 % while preserving high recall.
- Immutable audit trails on a permissioned blockchain allow instant rollback and compliance reporting.
About Corpora.ai: Corpora.ai is a frontier deep‑tech venture that builds secure, auditable AI systems for enterprises. Leveraging blockchain, advanced retrieval, and self‑critiquing generation, Corpora.ai empowers organizations to deploy autonomous agents that are trustworthy, interpretable, and resilient to adversarial threats. For more information, visit www.corpora.ai.