{"results":[{"id":"atms-de-kleer-1986","text":"de Kleer (1986) ATMS uses assumption-based environments and nogoods. TMS beats ATMS for EEM because revision matters more than multiple environments when the problem solver (LLM) produces 13-37% errors","truth_value":"IN","justification_count":0,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"continuity-human-problem","text":"The human cannot track what the LLM currently has in context. EEM solves this via visibility and persistence — the human can always inspect the current belief state. Short compaction cycles are better than large context windows","truth_value":"IN","justification_count":1,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"dual-path-architecture","text":"Dual-path retrieval: TMS path (pre-computed beliefs) + FTS path (source chunk search), merged by a third pass. This is how EEM is queried at scale. Each path stays within cognitive budget","truth_value":"IN","justification_count":1,"dependent_count":0,"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-epistemic","text":"Epistemic means not just facts but justified beliefs with truth values (IN/OUT), retraction cascades, contradiction records (nogoods), and derivation depth. This distinguishes EEM from RAG (which is external semantic memory but not epistemic)","truth_value":"IN","justification_count":0,"dependent_count":4,"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-memory","text":"Memory in Tulving's semantic memory category — persistent structured knowledge, not ephemeral context","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-replaces-confidence","text":"EEM replaces 'am I sure?' with 'is this justified?' — shifting from unreliable confidence to auditable justification chains","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-three-properties","text":"EEM is defined by three load-bearing properties: external (outside parameters), epistemic (justified with truth values), and memory (persistent semantic knowledge)","truth_value":"IN","justification_count":1,"dependent_count":0,"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":"eem-vs-parametric","text":"In-parameter knowledge has no audit trail. EEM makes 'how do you know that?' answerable by justification chain traversal. Of the six externality properties (separable, copyable, shareable, inspectable, editable, auditable), auditability is the most epistemically important","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-vs-rag","text":"RAG is external semantic memory but not epistemic. It retrieves content by similarity but has no justification chains, truth values, retraction cascades, or contradiction tracking. EEM adds the epistemic layer that RAG lacks","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"eem-works","text":"EEM measurably and dramatically improves LLM performance on domain tasks. The core research question is answered: yes","truth_value":"IN","justification_count":1,"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":"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":"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":"how-humans-use-eem","text":"Humans use EEM by: inspecting beliefs.md for current state, running reasons explain to understand why something is believed, challenging beliefs with reasons challenge, reviewing the network with reasons status, checking staleness with reasons check-stale. The key value is visibility — humans can see and audit what the system knows","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"how-to-start","text":"To start using EEM: (1) reasons init — creates reasons.db, (2) add premises from observations with reasons add, (3) add justified conclusions with --sl to link dependencies, (4) use reasons derive to find connections, (5) use reasons review-beliefs to audit, (6) retract when evidence changes and let cascades propagate","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null},{"id":"scale-evidence","text":"EEM scales from small domains (237 beliefs, aap-expert) to large enterprises (12,731 beliefs, redhat-expert). 40+ expert knowledge bases built across code, product, project, and domain-specific experts","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null}],"count":20,"limit":20,"offset":0}