{"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,"source_type":""},{"id":"beliefs-cli-vs-reasons-cli","text":"Two CLIs at different levels: beliefs CLI is a structured markdown KB with provenance and manual maintenance (simple, flat). reasons CLI (ftl-reasons) is a full TMS with automatic propagation, cascades, backtracking, and LLM-driven operations (powerful, dependency-aware). Use beliefs for independent facts, reasons for justified conclusions with dependency chains","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"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,"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":"dual-path-design-evidence","text":"Dual-path retrieval (TMS path for pre-computed beliefs + FTS path for source chunk search, merged by a third pass) achieves 98.5% A/B across 3,853 questions. Opus drops from 95.5% to 86% when mixing beliefs and document chunks in a single prompt; three focused passes achieve 100%.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"epistemic-honesty","text":"An epistemically honest EEM should distinguish between what it can demonstrate (structural consistency, self-correction, provenance tracking) and what it cannot demonstrate without external validation (absolute accuracy of numeric claims, generalizability to other implementations, superiority over alternatives). The TMS makes this distinction tractable by separating premises from derivations","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"evidence-depth-ceiling","text":"Beliefs beyond depth 8 do not survive review. Retraction rate: 0% at depth 0, rising to 100% at depth 9+. The universal TMS is wide rather than deep","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"ftl-reasons-is-tms","text":"ftl-reasons implements actual Doyle-style TMS architecture: SL justifications with antecedents and outlists, BFS propagation cascades with restoration, entrenchment-scored dependency-directed backtracking. LLMs fill the problem-solver role Doyle left open","truth_value":"IN","justification_count":2,"dependent_count":6,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"pass","source_type":""},{"id":"hybrid-tms","text":"ftl-reasons is a hybrid TMS: symbolic TMS handles structure (justifications, propagation, cascades, backtracking, challenge/defend) while LLMs handle semantic operations (derive generates beliefs, review-beliefs critiques them, contradiction detection finds nogoods)","truth_value":"IN","justification_count":1,"dependent_count":5,"challenges":[],"last_reviewed":"2026-05-29T17:30:21","review_result":"pass","source_type":""},{"id":"llm-as-problem-solver","text":"Putting an LLM in the TMS problem-solver slot (generator via derive, critic via review-beliefs and contradiction detection) is what Doyle's architecture prescribes. The open question is whether an LLM is a good problem solver, not whether using one is faithful to the design","truth_value":"IN","justification_count":1,"dependent_count":1,"challenges":[],"last_reviewed":"2026-05-29T17:30:21","review_result":"pass","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":"premises-are-trust-boundaries","text":"Premises (nodes with no justifications) are the trust boundaries of the network. The TMS cannot validate them — they are accepted by fiat. Every derived belief inherits the epistemic status of its premises. If a premise is wrong, everything that depends on it is structurally valid but false","truth_value":"IN","justification_count":1,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"restoration","text":"When a retracted node comes back IN, dependents are recomputed — no manual rederivation needed. The TMS tracks structure so restoration is automatic","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-29T17:30:21","review_result":"pass","source_type":""},{"id":"review-catches-llm-errors","text":"The review step catches the specific kinds of errors LLMs make during derivation. Four categories account for 100% of failures across 6 domains. Each maps to a repair strategy: smuggled premises → search-and-link (44-59% recoverable), superlatives → soften, false causal links → retract, domain conflation → retract. The 13-38% retraction rate validates that TMS review compensates for exactly the kind of errors LLMs produce.","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"structure-not-truth","text":"A belief being IN means its justification chain is structurally valid within the TMS — all antecedents are IN. It does not mean the belief is externally verified or true. Structure guarantees consistency, not correspondence with reality","truth_value":"IN","justification_count":1,"dependent_count":3,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"structure-not-truth-applies-to-site","text":"The expert.ftl2.com belief explorer demonstrates that TMS mechanics work (justification chains, IN/OUT propagation, retraction cascades) but does not prove the underlying claims are correct. A belief can be IN and fully justified within the system while being wrong, because all antecedents trace back to the same author's observations. Structure proves the tooling; external evidence proves the claims.","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"tms-addresses-circularity-partially","text":"The TMS partially addresses circularity through mechanisms that no self-reported benchmark has: retraction cascades mean correcting one error propagates to all dependents, nogoods record when claims contradict each other, check-stale detects when source material changes under beliefs. These make the system self-correcting but not externally validated","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"tms-doyle-1979","text":"Doyle (1979) designed Truth Maintenance Systems with SL justifications, propagation, retraction cascades, and an exogenous problem-solver slot. The TMS substrate is content-agnostic by design","truth_value":"IN","justification_count":0,"dependent_count":2,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""}],"count":21,"limit":20,"offset":0}