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Downloads

Stable, machine-readable artifacts from the Encyclopedia of Abstractions. Every registered file is copied byte-for-byte from its build output and covered by the public checksum manifest below.

Which graph download should I use?

Integrity / provenance

Catalog data (JSONL)

  • Prime abstractions — encyclopedia.primes.jsonl.gz (8.5 MiB) — One JSON object per prime abstraction (the detailed v2 record): slug, name, one_liner, categories, origin domain[s], aliases, and the full prose of every section (Core Idea, Structural Signature, Clarity, Manages Complexity, Abstract Reasoning, Knowledge Transfer, Examples, Structural Tensions, Solution Archetypes, Notes) plus fact anchors and word count. The complete text of the prime catalog in one line-delimited file — feed it to an assistant or index it directly. Gzip-compressed (gunzip it, or read directly with pandas / gzcat). Schema in encyclopedia.manifest.json.
  • Domain-specific abstractions — encyclopedia.domain_specific.jsonl.gz (6.4 MiB) — One JSON object per domain-specific abstraction (the detailed v2 record, with the concise v1 one_liner folded in): slug, name, one_liner, candidate domain / subdomain, and the full prose of every section (Core Idea, Scope of Application, Clarity, Manages Complexity, Abstract Reasoning, Knowledge Transfer, Notes). The second-layer catalog — abstractions that live inside a single field — in one file. Gzip-compressed (gunzip it, or read directly with pandas / gzcat). Schema in encyclopedia.manifest.json.
  • Solution archetypes — encyclopedia.archetypes.jsonl.gz (8.3 MiB) — One JSON object per solution archetype: slug, name, canonical family, source / related primes, components, mechanisms, the essence and compression statements, the problem signature (structural problem, triggers, symptoms, root tension, anti-signatures), the intervention signature (action logic, decision rules, target invariants, expected outcomes), and every prose section. Both generated and hand-curated archetypes, distinguished by a source field. Gzip-compressed (gunzip it, or read directly with pandas / gzcat). Schema in encyclopedia.manifest.json.
  • Schema manifest — encyclopedia.manifest.json (3.3 KiB) — Self-describing companion to the three catalog JSONL exports (uncompressed — it is tiny): schema version, the record shape (field list) for primes, domain-specific abstractions, and archetypes, per-file record counts at build time, and provenance notes (which corpus directories each file is read from).

Typed mixed DAG (canonical)

These are the authoritative schema-v2 graph artifacts. Typed IDs preserve Prime and Domain-specific identity; every logical relation and its proof fields are present. See the typed mixed-DAG consumer contract for direction and edge semantics.

  • Canonical typed nodes — mixed_dag.nodes.jsonl (1.1 MiB) — One schema-v2 record per abstraction, with collision-safe node_id, node_kind (prime or domain_specific), declared source type, canonical route, one-liner, and source path. Pair this streaming-friendly node table with mixed_dag.relations.jsonl. Validated by node.v2.schema.json.
  • Canonical logical relations — mixed_dag.relations.jsonl (4.2 MiB) — One schema-v2 record per logical relation, using typed child and parent IDs. Includes relation identity, edge type and effective flavor, qualifier, short proof, full justification, condition or exception, and normalized mutual endpoint explanations. Validated by relation.v2.schema.json.
  • Canonical indexed graph bundle — mixed_dag.json (6.9 MiB) — The same typed nodes and logical relations in one JSON object, plus collision-safe by_child and by_parent indexes. Use this when loading one in-memory graph is more convenient than joining the two JSONL streams.
  • Canonical graph manifest — mixed_dag.manifest.json (5.1 KiB) — Counts, graph diagnostics, edge vocabulary and direction, frozen-release validation, topology and identity seals, byte sizes and SHA-256 hashes, schema locations, and the status of the prime-only compatibility projection.
  • JSON Schema — typed DAG node v2 (1.6 KiB) — Draft 2020-12 JSON Schema for every record in mixed_dag.nodes.jsonl, including cross-field constraints among typed identity, declared type, route, and canonical source path.
  • JSON Schema — typed DAG logical relation v2 (3.9 KiB) — Draft 2020-12 JSON Schema for every record in mixed_dag.relations.jsonl, including direction, allowed endpoint-kind pairs, flavors, qualifiers, proof fields, and mutual normalization.

DAG convenience export

A denormalized, node-oriented rendering of the same canonical mixed graph. Choose this for document retrieval or LLM ingestion; choose the canonical node/relation streams above for graph computation.

  • Node-oriented mixed-DAG convenience export — hierarchy.jsonl (8.8 MiB) — One JSON record per typed prime or domain-specific abstraction carrying its node ID, canonical route, identity metadata, curated one_liner, first Core Idea paragraph, and every parent relation with full reason text. The whole mixed DAG is recoverable, but this denormalized shape is optimized for browsing, LLM ingestion, and node-at-a-time analysis rather than graph database loading. Schema documented in hierarchy.manifest.json.
  • Convenience-export manifest — hierarchy.manifest.json (2.9 KiB) — Self-describing companion to hierarchy.jsonl: schema version, field definitions, relation storage rule, direction, edge vocabulary, corpus and relation counts, release seals, byte size, and SHA-256 hash.

Prime-only DAG (legacy compatibility)

Compatibility only — not canonical. These files intentionally omit all domain-specific nodes and every non-prime-to-prime relation. The projection manifest gives the exact current omissions. New consumers should not start here.

Learnability

  • Learnability data export — learnability.jsonl (6.2 MiB) — One JSON object per prime: slug, name, tier, curriculum order, the lowest age at which a faithful explanation is possible, all 5 age-graded explanations (ELI5/10/15/18/specialist), and 3 levels of everyday-English name (ELI5/10/15). Includes na_reasons for the 9 K-unteachable primes where some lower levels genuinely cannot be reached. Intended for LLM consumption — feed it to an assistant to build adaptive curriculum, run teach/test exercises, generate questions or worked examples, or build a learner-or-teacher game. Schema documented in learnability.manifest.json.
  • Schema manifest — learnability.manifest.json (3.4 KiB) — Self-describing companion to learnability.jsonl: schema version, field definitions, level targets (word-count budgets and vocabulary constraints per ELI level), source-file hashes, tier-distribution stats, and intended-use notes.

Companion writing

  • The Nothingness Ladder (363.9 KiB) — A ~16,000-word standalone essay structured as a four-rung hierarchy of what "nothing" could mean (quantum vacuum → topological vacuum → metageometric void → logical nullity). Not part of the encyclopedia catalog itself, but a worked example of the kind of cross-domain abstraction the catalog tries to surface. Linked from the About page.

Miscellaneous

  • Distinctiveness pipeline — prime_labeling.py (40.2 KiB) — The re-runnable script that computes each prime's distinctiveness / neighborhood-density and the families (incremental + batch-recompute modes). See the conceptual paper Distinctiveness and the Neighborhood Structure of Abstraction Space.
  • Runtime-scaffolding experiments — raw data (projects 02–04) (1.3 MiB) — Every prompt, model output, private manifest / blinded-grade mapping, coverage key, grade-set, and score from the A/B/C/B′ verb-engine, low-coverage, and faithfulness experiments, plus the re-runnable harness scripts. The reproducibility bundle behind the retrospective The Limits of Runtime Scaffolding.
  • Operator-driven prime discovery — raw data (2.3 MiB) — The reproducibility bundle behind the conceptual papers Operator-Driven Discovery of Prime Abstractions and The Operator Compendium. Every quantitative claim's raw material: the per-application crank outputs for all fourteen operators (input prime, proposed candidate, NEW/COLLAPSE verdict, nearest existing prime) — from which every NEW-rate, fixed-point census, and convergence count can be independently recomputed; the per-operator result ledgers (prose + a machine-readable operators_summary.csv); the fixed-point / already-X audit censuses behind the saturation gradient; the 128 candidate-prime stubs the search surfaced; the ~295 candidate hierarchy edges harvested as a byproduct; the retained hard-negatives; and the frozen Stage-0 prompt for every operator. A README maps each file to the paper claim it substantiates. Snapshot 2026-07-04, corpus frozen at 1,325 primes; acceptance counts provisional pending final human curation (the raw data itself is final).
  • Learnability scoring + ELI ladder — implementation bundle (42.4 KiB) — The five re-runnable Python scripts behind the By-Learnability page: the curriculum scorer (Kahn's topological sort with difficulty priority queue), the ELI ladder setup + consolidator (triangulated 3-generator + 1-judge pipeline), the page builder, and the v0.5 weight-tuning experiment. Each script is single-file and dependency-free. The conceptual paper Curriculum Construction over a Prime Catalog is included, plus a README explaining how the pieces fit together and what external inputs (Kuperman 2012, Brysbaert 2014) are required.