What is it about?
As Large Language Models are increasingly deployed as autonomous information extractors, their susceptibility to hallucinations and extraction errors poses critical risks for downstream enterprise workflows. Our work introduces a deterministic safety architecture designed to eliminate single-point extraction failures without compromising processing speed.
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Why is it important?
LLM-based extractors operating on uncurated streams introduce non-deterministic errors that undermine downstream data integrity. We present a safety architecture combining append-only immutable memory logs with corroboration quorums—a multi-agent consensus verification protocol. By requiring multi-perspective validation prior to state commit, the framework isolates fallible extraction outputs, bound-checks probabilistic drift, and enforces cryptographically verifiable audit trails across dynamic enterprise knowledge graphs.
Perspectives
Key Highlights 1. Immutable Logs: Prevents silent state corruption by enforcing strict append-only storage for raw LLM outputs. 2. Corroboration Quorums: Evaluates multi-agent extraction consensus to filter out single-model hallucinations before database writes. 3. Verifiable Auditability: Maintains end-to-end cryptographic lineage from raw source to final structured knowledge.
Uday Bhanu Prasad Kanagala
Read the Original
This page is a summary of: Corroboration Quorums on Append-Only Memory: A Safety Architecture for Fallible LLM Extractors, August 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3806096.3844832.
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