| --- |
| schema_version: "1.0" |
| type: eem |
| project_name: eem-expert |
| domain: |
| - external-epistemic-memory |
| - truth-maintenance-systems |
| - llm-knowledge-management |
| license: mit |
| base_network: null |
| source_repos: |
| - benthomasson/ftl-reasons |
| beliefs_total: 90 |
| beliefs_in: 90 |
| beliefs_out: 0 |
| premises: 49 |
| derived: 41 |
| nogoods: 0 |
| generator: ftl-reasons/0.42.0 |
| --- |
| |
| # EEM Expert |
|
|
| Expert knowledge base for explaining External Epistemic Memory (EEM) to humans and LLM-based agents. Contains 90 justified beliefs covering what EEM is, how it works, why it matters, and empirical evidence for its effectiveness. |
|
|
| ## What is this? |
|
|
| This is an **External Epistemic Memory** (EEM) — a model-agnostic knowledge base that any LLM can use via the `reasons` CLI or tool calling. Unlike a LoRA or fine-tune, this knowledge is not baked into model weights. It is external, inspectable, correctable, and works with any model. |
|
|
| ## Stats |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Total beliefs | 90 | |
| | Status | 90 IN / 0 OUT | |
| | Premises (observations) | 49 | |
| | Derived (justified conclusions) | 41 | |
| | Nogoods (contradictions) | 0 | |
| | Retraction rate | 0% | |
| | Max derivation depth | 5 | |
|
|
| ## Domain Coverage |
|
|
| - **What EEM is**: three load-bearing properties (external, epistemic, memory), formal definition |
| - **How EEM differs from alternatives**: vs RAG, vs context/conversation history, vs parametric knowledge, vs knowledge graphs |
| - **TMS architecture**: Doyle 1979 foundations, SL justifications, retraction cascades, nogoods, backtracking |
| - **Empirical evidence**: ablation studies, dual-path validation, confidence unreliability, model compensation, six-domain validation |
| - **Design principles**: derive-then-review, cognitive budget, wide-not-deep, generate-and-critique |
| - **Practical workflows**: expert pipeline, how agents use EEM, how humans use EEM, multi-agent belief tracking |
| - **Construction & cost**: amortization argument, automated overnight construction, construction cost measurements |
| - **Staleness & maintenance**: staleness detection, source change tracking, stale belief workflows |
| - **Getting started**: installation, CLI interface, quick start, HTTP endpoint access |
|
|
| ## How to Use |
|
|
| ### Import into a reasons database |
|
|
| ```bash |
| reasons init |
| reasons import-json network.json |
| ``` |
|
|
| ### Query beliefs |
|
|
| ```bash |
| reasons search "what is EEM" |
| reasons explain eem-definition |
| reasons show eem-three-properties |
| ``` |
|
|
| ### Use as an MCP tool or CLI |
|
|
| Any LLM agent that can call `reasons search`, `reasons show`, and `reasons explain` can use this knowledge base. The agent does not need to be told it is an expert — the knowledge base speaks for itself (see belief `expert-prompt-paradox`). |
|
|
| ## Key Beliefs |
|
|
| | Node | Summary | |
| |------|---------| |
| | `eem-definition` | EEM is knowledge that lives outside the model, carries its justifications, and lets you understand how the system knows what it knows | |
| | `eem-three-properties` | External, epistemic, memory — three load-bearing properties | |
| | `eem-works` | EEM measurably and dramatically improves LLM performance on domain tasks | |
| | `evidence-dual-path` | Opus + dual-path achieves 98.5% A/B across 3,853 questions | |
| | `evidence-retraction-rate` | 13-37% of derived beliefs retracted per review round — self-correction works | |
| | `confidence-unreliable` | LLM self-assessed confidence does not track accuracy (r=-0.182 to r=0.219) | |
| | `ftl-reasons-is-tms` | ftl-reasons implements Doyle-style TMS with LLMs as problem solvers | |
|
|
| ## Sources |
|
|
| Built from exploration of [benthomasson/ftl-reasons](https://github.com/benthomasson/ftl-reasons) and empirical studies across 40+ expert knowledge bases ranging from 237 to 13,511 beliefs. |
|
|
| ## Files |
|
|
| | File | Description | |
| |------|-------------| |
| | `network.json` | Full belief network (machine-readable, portable) | |
| | `reasons.db` | SQLite database (gitignored, regenerate with `reasons import-json network.json`) | |
| | `CLAUDE.md` | Agent instructions for using this knowledge base | |
|
|
| ## Quality |
|
|
| - All 90 beliefs are IN (none retracted) |
| - 49 premises grounded in direct observations and published research |
| - 41 derived beliefs justified from premises via SL justifications |
| - 0 nogoods — no contradictions detected |
| - Built and reviewed using ftl-reasons derive and review-beliefs pipeline |
|
|
| ## Limitations |
|
|
| - Focused on EEM concepts and ftl-reasons implementation — does not cover alternative TMS implementations in detail |
| - Empirical evidence drawn primarily from code-expert use cases |
| - No ATMS or assumption-based beliefs (single-context TMS only) |
| - PostgreSQL multi-tenant patterns not covered |
|
|
| ## Authors |
|
|
| - Ben Thomasson ([@benthomasson](https://github.com/benthomasson)) |
|
|
| ## License |
|
|
| mit |
|
|
