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  "map_content": "AnchorChain: Immutable Referencing of AI Memory States through Blockchain-Indexed Volumetric Data Linking \r\nDr Craig S. Wright University of Exeter Business School Exeter, United Kingdom\r\nAbstract\u2014Memory integrity in AI systems remains a foundational weakness in both training reproducibility and operational trustworthiness. Large-scale vector data, embeddings, and internal memory traces are discarded, rewritten, or silently drift over time without cryptographic anchors or verifiable context.\r\nThis paper introduces AnchorChain, a mechanism for binding AI memory and volume-based data structures (e.g. embedding matrices, long-term memory vector stores) to blockchain-indexed anchors. Each memory state is summarised into a cryptographically verifiable Merkle root, indexed in a deterministic fashion, and immutably committed to a transaction on a Bitcoin-based ledger.\r\nThis enables traceable recovery, rollback, and accountability in AI training and inference workflows\u2014resolving the problem of memory state erosion across system restarts, deployments, or trust boundaries. The system supports deterministic reference resolution, anchor-based recall indexing, and verifiable memory finality, solving a critical missing reliability gap in AI-driven software engineering.",
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  "timestamp": "2026-02-06T13:45:21.000Z",
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