{"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":"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},{"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":"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":"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},{"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":1,"dependent_count":6,"challenges":[],"last_reviewed":null,"review_result":null},{"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":4,"challenges":[],"last_reviewed":null,"review_result":null},{"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":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},{"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":null,"review_result":null},{"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},{"id":"wide-not-deep","text":"The universal TMS is wide rather than deep. Depth-8 ceiling is structural. Experiments reset derivation depth by providing new depth-0 observations","truth_value":"IN","justification_count":1,"dependent_count":0,"challenges":[],"last_reviewed":null,"review_result":null}],"count":13,"limit":20,"offset":0}