{"results":[{"id":"amortization-argument","text":"EEM construction is expensive but amortizes. ~$300 Sonnet for 13,511 beliefs. Each query costs ~$0.01. Breakeven at 100-250 queries. After that, every query is cheaper than re-reading source documents from scratch.","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"invalid","source_type":""},{"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,"source_type":""},{"id":"construction-cost-measured","text":"EEM construction cost measured for enterprise scale (6 departments, 5,366 sources, 13,511 beliefs): ~$300 at Sonnet pricing, ~$1,500 at Opus pricing. Dominant cost is the summarize step (~98M tokens). Per-query breakeven at 100-250 queries — after that, every query is cheaper than re-reading source documents.","truth_value":"IN","justification_count":0,"dependent_count":2,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"continuity-human-problem","text":"The human cannot track what the LLM currently has in context. Context windows are opaque and compaction destroys justification networks. EEM solves this via visibility and persistence — the human can always inspect the current belief state regardless of what the model has in context.","truth_value":"IN","justification_count":2,"dependent_count":1,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"invalid","source_type":""},{"id":"credibility-is-presentation-problem","text":"The credibility gap on llmeem.ai is a presentation problem, not a substance problem. The evidence (eval harnesses, question sets, raw results, methodology writeups) exists but is not linked or publicly accessible. Fixing credibility requires linking to existing evidence, not generating new evidence.","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"cross-model-portability","text":"EEM works across model providers and sizes. The same belief network can be queried by Claude, Gemini, local models, or any LLM that can read text. Model upgrades, provider swaps, and cost optimization (Opus→Haiku) preserve all knowledge. The beliefs are plain text with structure — no model-specific format.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"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":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"id":"eem-cli-interface","text":"The reasons CLI provides: reasons init (create database), reasons add (add beliefs with --sl for justifications, --source for provenance), reasons retract (mark OUT with cascade), reasons assert (mark IN with restoration), reasons search (semantic search), reasons show (full details), reasons explain (justification trace), reasons derive (generate new beliefs), reasons review-beliefs (audit), reasons challenge/defend (dialectical argumentation), reasons check-stale (source change detection), reasons nogood (record contradictions), reasons export-markdown (beliefs.md output), reasons compact (token-budgeted summary).","truth_value":"IN","justification_count":0,"dependent_count":4,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"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":"2026-05-30T07:02:40","review_result":"unnecessary","source_type":""},{"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,"source_type":""},{"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":6,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"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":"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,"source_type":""},{"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":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"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":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"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":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"id":"eem-vs-knowledge-graphs","text":"Knowledge graphs store entities and relationships (what exists). EEM stores justified beliefs (what is believed and why). Knowledge graphs have no retraction cascades, no derivation depth, no contradiction tracking. When a fact is wrong, the graph doesn't know what else depends on it. EEM does. Every ontology is an implicit epistemology — it treats beliefs as facts, which works until they're wrong.","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"id":"eem-vs-parametric","text":"In-parameter knowledge has no audit trail. EEM makes 'how do you know that?' answerable by justification chain traversal. EEM's externality provides six properties: separable, copyable, shareable, inspectable, editable, auditable. Auditability is what distinguishes EEM from other external stores — it is the epistemic property.","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"invalid","source_type":""},{"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":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"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":3,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"unnecessary","source_type":""}],"count":41,"limit":20,"offset":0}