{"results":[{"id":"cognitive-budget","text":"Cognitive budget principle borrowed from graphics frame budgets: decompose work into focused passes (TMS pass, RAG pass, merge pass) each within the model's attention budget. Mixing beliefs and document chunks in a single prompt degrades performance (Opus drops 95.5% to 86%); three focused passes achieve 100%","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"confidence-unreliable","text":"LLM self-assessed confidence does not track accuracy. Confirmed across 4 models: Sonnet r=0.198, Opus r=-0.182 (worse than random), Flash r=0.219, Pro r=0.121. Answer and confidence come from the same process — same structural flaw as human overconfidence (Kahneman)","truth_value":"IN","justification_count":0,"dependent_count":2,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-compensates-model-size","text":"EEM compensates for model size — smaller models with EEM match larger models without it","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-definition","text":"External Epistemic Memory (EEM) is knowledge that lives outside the model, carries its justifications with it, and lets you understand how the system knows what it knows","truth_value":"IN","justification_count":0,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-external","text":"External means outside model parameters, in a separate substrate. Survives compaction, model swaps, session boundaries. Inspectable, editable, auditable, shareable across models","truth_value":"IN","justification_count":0,"dependent_count":5,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-vs-context","text":"Conversation history and context windows are ephemeral — lost at session boundaries, destroyed by compaction. EEM persists across sessions and model swaps. Context compaction destroys justification networks (quantified across 33 measured compaction events)","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"evidence-model-compensation","text":"EEM compensates for model size: Sonnet+beliefs approximates Opus without beliefs. Haiku with dual-path achieves 94% A+B, matching Opus at 98%","truth_value":"IN","justification_count":0,"dependent_count":1,"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":"self-critique-harmful","text":"LLM revision based on self-critique makes answers worse: Sonnet -11pp, Flash -21pp, Pro -56.5pp. Self-critique fails because the same model that made the error evaluates the error","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null}],"count":9,"limit":20,"offset":0}