Hierarchical Temporal Logging System for AI Agents: From Nanoseconds to the Heat Death of the Universe
Abstract
We present a novel architecture for persistent memory in AI coding agents based on hierarchical temporal logging across 21 time scales: from one nanosecond to the estimated heat death of the Universe (10^100 years). To our knowledge, this is the longest temporal scope ever proposed for any information architecture. Implemented for Claude Code (Anthropic) using Markdown files and MCP servers. Version 2.0 — revised per Diamond Standard (30-block academic structure). Reviewed by multi-model AI Consilium.
Fields of Study & Subjects
Software System Performance and Reliability
Scientific Computing and Data Management
Software Engineering Research
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 15 March 2026
- Language
- EN
- Document type
- Preprint