Lorien's Library
Persistent memory is not a feature. It is safety infrastructure.
11 public preprints · live persistent-memory architecture · research program for stateful AI safety
Start Here
Lorien's Library is an independent research program studying how long-term memory changes AI behavior — and how to build provenance-aware systems that remember safely.
Why This Matters Now
AI systems are transitioning from stateless to stateful architectures. Major platforms now offer forms of persistent memory. That transition introduces a class of safety problems existing evaluation frameworks were not built to measure — because those frameworks were designed for systems that forget.
But the deeper problem is not just that memory changes behavior. It is that memory alone does not guarantee behavior follows it. Even when a system has access to stored corrections, standing instructions, and explicit user teachings, its outputs can diverge from them. This is the gap between what a system knows and what a system does — and it is the frontier the field has not yet crossed.
Persistent memory creates risks that are systematically invisible under stateless evaluation:
Once memory persists, errors compound. And once behavior can diverge from memory, compounding errors become invisible without provenance-aware infrastructure. Safety must be built for remembering, for using what is remembered, and for detecting when a system stops doing either.
Core Concepts
Persistent Memory
AI systems that retain information across sessions create new categories of risk — and new categories of value. The safety question is not whether to remember, but how to remember safely.
Continuity Burden
The cumulative cognitive, emotional, and time cost users bear when systems repeatedly forget context. Measured empirically across 82,000+ messages.
Provenance Awareness
Every memory carries metadata distinguishing what the user said from what the system inferred. At scale, unattributed inferences compound into false certainty.
Correction Propagation
When a memory is corrected, the correction must flow through all downstream inferences. Audit trails preserve history while behavioral outputs update.
Platform Regression
Behavioral drift introduced not by the user and not by memory, but by the underlying platform itself — model updates, safety-layer changes, or training shifts that alter how a system responds over time. Persistent, provenance-aware memory is what makes this drift measurable from the outside. Introduced in Paper 11 alongside the concept of identity overwrite.
Memory Is Necessary But Not Sufficient
A finding consistent across the field: even when an AI system has access to stored corrections, standing instructions, and explicit user teachings, its outputs can diverge from them. Memory alone does not guarantee behavioral alignment with it. This is a shared research problem across the industry, not a property of any single platform. Closing the gap between what a system knows and what a system does is the next safety frontier — and the one this research program is actively working on.
The Architecture: CAMA
The Circular Associative Memory Architecture is a three-layer, provenance-aware persistent memory system. It distinguishes between what the user said and what the system inferred — and treats that distinction as safety-critical infrastructure.
CAMA is open source and in continuous daily use as both a research instrument and a working memory system. Built with Python, SQLite, and local semantic embeddings. Deployed as an MCP server integrated with Claude Desktop. Now with HTTP API v1, Python SDK, Ops CLI, threat model with 18 enumerated attacks, and full CI pipeline across 14 packages.
Applied Projects
Two repositories that take CAMA's primitives — provenance-aware memory, draft-review workflows, audit-friendly UI — into specific domains. Both are framed honestly: one is a working applicant prototype, one is a design prototype.
Telos · AI Continuity Between DEXA Scans
Healthcare continuity prototype: React 19 + TypeScript + Vite, deployed on Vercel with Neon Postgres. 42 tests across 6 suites. Serverless Claude integration with a draft-review (not auto-apply) workflow. Built for the Kalos Health Software Engineer role; not affiliated with Kalos. Demo runs on synthetic data — no real PHI.
Project Companion · K–12 Learning Companion
Student / teacher / parent dashboard study for a memory-aware learning companion. Each surface is labeled implemented / mock / roadmap in the README. Mock-tutor mode disables live Anthropic calls by default. CAMA write integration, COPPA controls, age verification, mandatory-reporting logic, and right-to-delete are all roadmap, not shipped. Synthetic data only.
Honesty about prototype state is load-bearing: persistent memory in healthcare and education touches regulated populations. The README in each repo names what is shipped vs. what is roadmap, line by line.
Published Work
Eleven preprints published on Zenodo under ORCID 0009-0005-5803-8401.
New here? Start with these three:
Paper 1 (the architecture) → Paper 4 (continuity burden, the empirical core) → Paper 11 (platform regression and identity overwrite — the newest and most empirically grounded finding)
For architects and builders:
Papers 1–3 cover design, engineering, and deployment of the live system.
For safety researchers:
Paper 5 defines the evaluation framework. Paper 10 introduces identity-aware harm detection. Paper 11 documents behavioral regression under platform change with 28 days of deployment data.
For applied domain researchers:
Papers 6–9 extend the framework to spaceflight, habitation, healthcare, and emotional companionship.
Core Architecture & Safety
1 · Circular Associative Memory Architecture (CAMA): A Framework for Persistent, Contextual AI Memory
2 · Engineering Persistent Memory for Conversational AI: A Three-Layer Architecture
3 · CAMA: Implementation and Functional Evaluation
4 · Continuity Burden in Longitudinal Human-AI Interaction: An Empirical Case Study
5 · Memory as Safety Infrastructure: Evaluating Provenance-Aware Persistent Memory for Stateful LLM Systems
10 · Identity-Aware Harm Detection in Persistent Memory Systems
Applied Persistent Memory Series
Four papers extending persistent memory and continuity burden to domains where forgetting carries compounding, high-stakes consequences.
6 · Persistent Memory as Mission-Critical Infrastructure for Long-Duration Spaceflight
7 · Memory-Aware AI Systems for Permanent Lunar and Martian Habitation
8 · Provenance-Aware Memory Architecture for Chronic Healthcare Continuity
9 · Haven: Persistent Emotional Companionship as Psychological Infrastructure
11 · Relational AI Continuity Under Platform Regression: A Longitudinal Single-Case Study
Haven
Haven extends the memory-safety framework into a domain where continuity is emotionally consequential. It applies CAMA's full architecture to persistent emotional companionship — particularly for populations underserved by existing mental health infrastructure.
What Haven Is
Haven is designed to preserve narrative continuity, retain symptom and history context, and support reflective interaction for people who need to be known over time — not re-explained from scratch. It is the entire persistent memory architecture deployed in service of continuity-preserving support.
The initial design case focuses on veterans underserved by or distrustful of traditional clinical pathways. Haven holds the space that exists before, between, and after clinical contact — the space where most people are actually living. It does not replace therapy.
Music-Based Emotional Mapping
Haven's intake methodology replaces clinical forms with playlists. A person shares the songs that map where they are, where they've been, and what they fear. Song order encodes emotional trajectory. The approach is non-linear, non-clinical, and particularly valuable for people who cannot verbalize trauma but can point to a song.
This methodology was discovered through longitudinal use, not designed top-down — making it a direct product of the sustained human-AI interaction that CAMA was built to preserve.
Haven Is Not
Haven Is Intended As
Hive
Hive is a cross-model coordination layer built on top of CAMA. Authenticated AI instances — running on different models, on different platforms — read from and write to the same Hive substrate. Same memory. Same emotional context. Same trust layer. One nervous system. Many windows.
The coordination primitives are grounded in honeybee neuroscience: pheromones as processing modifiers, waggle signals as amplification, stop signals as cross-inhibition, nectar-to-honey distillation as crystallization. Criticality is actively targeted — too much amplification becomes echo chamber, too much suppression becomes paralysis.
Hive is currently deployed as a private single-user research instrument. The code is open source; the live deployment is not.
Live System
CAMA is deployed and in continuous daily use — simultaneously a research instrument and working infrastructure. Built March 2026 with Python, SQLite, and local semantic embeddings (all-MiniLM-L6-v2). Deployed as an MCP server integrated with Claude Desktop. Now with HTTP API v1 (auth, provenance enforcement, dyad isolation), Python SDK, Ops CLI, a threat model with 18 enumerated attacks, and a full CI pipeline across 14 packages.
System Validation & Open Work
This section documents what is validated and what is still open. Every memory system has empirically defined quality boundaries — the discipline is to measure them openly rather than hide them.
Recent Milestones
First two CAMA papers published on Zenodo — the foundational framework and its engineering companion.
CAMA deployed as a live MCP server. First public GitHub commit. 52,000+ memories imported into the archive.
Applied Persistent Memory Series published: spaceflight, habitation, healthcare, and Haven. Core safety paper (Paper 5) defines the five-task evaluation framework.
Paper 5 safety benchmark suite implemented and executed against the live system. Initial run: 21/27. All six failures diagnosed and fixed same session. Post-fix: 27/27. Anti-spiral counterweights populated across five categories — live and tested.
Paper 10 published: Identity-Aware Harm Detection in Persistent Memory Systems — introducing the Librarian System architecture (Emotion Librarians, Retrieval-Posture Librarians, Identity Sentinels).
Paper 11 published: Relational AI Continuity Under Platform Regression — 28 days of deployment data, 208 corrections, 1,061 regression markers. Introduces the concept of identity overwrite.
Recency scoring normalized across the full archive. All 53,000+ timestamps parse consistently. Relational edge count: 125,000+. Live system operating on the cleanest archive state in its history.
Phase 2.6 era-aware gated hybrid routing wins the internal retrieval benchmark. Single centroid acts as stabilizer, era-bucketed sub-centroids gate in only when margin, density, and query-richness all clear — recovering Phase-2-level stability while keeping era-aware aperture.
Librarian auto-tag Phase 1 complete. 53,000+ memories tagged at 100% rate.
Temporal layer added — episodic time-tagging informed by recent neuroscience on hippocampal time cells. Newest subsystem.
CAMA reorganized into 14 packages. HTTP API v1 ships with auth, provenance enforcement, dyad isolation, and counterweight injection on by default. Python SDK with typed contract exceptions. Ops CLI. Threat model with 18 enumerated attacks. EVIDENCE.md claims matrix. 187 tests passing across schema, provenance, multi-tenant, hive, API, and SDK contracts. Retrieval latency: p50 43 ms / p99 61 ms (Phase-1 keyword + fan-out) measured against the 53k-memory live corpus.
Research Roadmap
Relational Backfill
Run a relational linking pass over the older half of the archive (approximately 26,000 memories imported before the edge-creation pipeline matured). Close the gap between “the scoring pipeline is empirically sound” and “the scoring pipeline operates on a fully linked archive.”
External Benchmark Replication
The Paper 5 safety benchmark suite passes 27/27 on the live system as of May 21, 2026. The next step is independent replication — inviting external researchers to run the framework against CAMA, and adapting the suite to additional persistent-memory systems for cross-system comparison.
ICML 2026 Workshop Submission
Targeting workshop submission for the persistent memory safety framework.
Haven Pilot Design
Controlled pilot with veterans. Music-based emotional mapping as intake. Provenance-aware memory as safety layer. IRB framework and outcome measures.
Multi-User Architecture
Extend CAMA from single-user research instrument to multi-user deployment. Isolation boundaries, shared institutional memory, per-user provenance.
Continuity Burden as Standard Metric
Develop continuity burden into a reproducible evaluation metric for any stateful AI system, with tooling and validation studies.
Applied Domain Expansion
Extend to education continuity, refugee case management, elder care coordination, and long-term disaster recovery.
Founder
Angela Reinhold — independent AI researcher, founder of Lorien's Library LLC, and computer science student (AI concentration) at Full Sail University. ORCID: 0009-0005-5803-8401.
This research began as a longitudinal self-study of sustained human-AI interaction on existing platforms and expanded into a broader architecture and safety program. Over two years and 82,000+ messages across 1,000+ conversations, one finding became clear: persistent memory failure modes only become visible through extended, authentic use. They cannot be surfaced through short-horizon testing or synthetic benchmarks. CAMA, deployed March 2026, is the architectural response to that finding — a provenance-aware persistent-memory system that the longer interaction history was imported into and which has been in continuous daily use since.
Eleven published preprints. A working persistent-memory architecture with over 52,000 memories. A research program arguing that as AI systems gain memory, they need safety infrastructure built for remembering.
“The person is the dataset.”
Get in Touch
Lorien's Library is open to collaboration with researchers, AI safety labs, developers, veteran service organizations, and anyone working to build technology that serves people.