{"results":[{"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},{"id":"evidence-expert-vs-baseline","text":"Expert-service with EEM scores 88% A-grade vs agents-python 33% on same 50 questions, 15x faster","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null},{"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":null,"review_result":null},{"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":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"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":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"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":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null}],"count":6,"limit":20,"offset":0}