{"results":[{"id":"eem-external","text":"External means outside model parameters, in a separate substrate. Survives compaction, model swaps, session boundaries. Six properties: separable (exists independently of the model), copyable (can be duplicated), shareable (multiple agents can access it), inspectable (humans can read it), editable (humans can modify it), auditable (justification chains are traversable).","truth_value":"IN","justification_count":0,"dependent_count":6,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"evidence-beliefs-ablation","text":"Beliefs alone outperform beliefs + expert prompt: Opus 100% vs 94.2% (+5.8pp), Sonnet 94.2% vs 91.8% (+2.4pp). Adding expert prompt hurts — agent trusts its 'expertise' instead of consulting the knowledge base","truth_value":"IN","justification_count":0,"dependent_count":2,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"evidence-expert-vs-baseline","text":"Expert-service with EEM scores 88% A-grade vs an agent pipeline 33% on same 50 questions, 15x faster","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"expert-agent-builder-repo","text":"expert-agent-builder automates the knowledge pipeline: fetch docs → generate entries → extract beliefs → derive → review. Install: pip install expert-agent-builder or uv tool install expert-agent-builder. Source and issues: https://github.com/benthomasson/expert-agent-builder","truth_value":"IN","justification_count":0,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"expert-prompt-paradox","text":"Telling an agent it is an expert reduces belief utilization. The humble generic prompt produces better results because the agent consults the knowledge base instead of trusting its 'expertise'","truth_value":"IN","justification_count":1,"dependent_count":1,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"id":"how-agents-use-eem","text":"LLM agents use EEM by: querying beliefs via search/show/explain before answering, citing node IDs for auditability, running derive to generate new beliefs from existing ones, running review-beliefs to self-audit, recording nogoods when contradictions appear. The agent does not need to be told it is an expert — the knowledge base speaks for itself","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"unnecessary","source_type":""},{"id":"http-endpoint-access","text":"EEM is accessible via a single HTTP GET at https://expert.ftl2.com/public/eem-expert/beliefs — no Python library, no CLI installation, no database copy, no setup. Three formats available: HTML (human-browsable), Markdown (agent-readable), JSON (machine-readable). Any agent that can fetch a URL can consume justified beliefs immediately.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"import-agent-implementation","text":"import-agent command imports another agent's beliefs with SL justifications including agent:active as antecedent. Node is IN iff agent is active AND original belief is justified. Implemented in ftl-reasons CLI.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"model-stacking","text":"Multi-pass agent pattern: Model A generates candidates → TMS records with provenance → Review critiques (machine + human) → Model B receives validated beliefs → Model B derives new beliefs → Review critiques derivations → Repeat. Each level is a full model pass with fresh context and critique pipeline as quality gate","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-29T17:30:21","review_result":"invalid","source_type":""},{"id":"model-stacking-evidence","text":"Multi-pass agent pattern observed: Model A generates candidates, TMS records with provenance, review critiques (machine + human), Model B receives validated beliefs, Model B derives new beliefs. Demonstrated in expert-build pipeline where Sonnet summarizes sources, then Sonnet derives, then Sonnet reviews — each pass gets fresh context with the TMS as the persistent layer between passes.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"multi-agent-beliefs","text":"Multi-agent TMS: import-agent imports another agent's beliefs with SL justifications including agent:active as antecedent. Node is IN iff agent is active AND original belief is justified. Doyle-style truth maintenance across agents","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-29T17:30:21","review_result":"invalid","source_type":""},{"id":"reasons-db-vs-beliefs-md","text":"Architecture pattern in practice: reasons.db (SQLite) is the primary store for all structural operations — add, retract, derive, review, justify. beliefs.md is an export format for querying — fast, human-readable, grep-able, used as agent context. Both kept in sync via reasons export-markdown.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"scale-data","text":"40+ expert knowledge bases built across domains. Smallest: aap-expert (237 beliefs). Largest: redhat-expert (12,511 nodes across 6 departments/expert agents, 11,897 IN after repair). Domains include enterprise products, codebases, research papers, certification curricula, cloud infrastructure (AWS, GCP, Azure, OpenShift, bare-metal, Hetzner).","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""}],"count":13,"limit":20,"offset":0}