Every ARC-specific term used across the project, alphabetized. This doubles as onboarding and as a search index.
Adapter
The standardized interface for a model backend. Any class implementing adapters.base.ModelAdapter can be driven by the canonical pipeline. Built-ins: exemplar, heuristic, echo, command, llama_cpp_http, openai_compatible.
ANCF (ARC Neuron Canonical Format)
A binary envelope wrapping a GGUF file with embedded JSON metadata. Layout: 4-byte magic ANCF, 4-byte version, 8-byte meta-len, 8-byte gguf-len, metadata bytes, GGUF bytes. Used for canonical model-artifact storage.
Arc-RAR bundle
A ZIP archive containing a promoted candidate's full lineage: manifests, receipts, PyTorch checkpoint, GGUF export, exemplar artifact, and a SHA-256 index. Readable in isolation via runtime.learning_spine.read_arc_rar_manifest.
Archive-only A Gate v2 decision state. The candidate did not beat the incumbent (or violated a regression ceiling) but is preserved in the scoreboard for future analysis. The incumbent is untouched.
Attribution boundary The governance principle that each component, capability, and decision must trace back to a single owner. Merging two receipts into one breaks attribution and is rejected.
Benchmark suite The 142-task evaluation set organized across 14 capability families: reasoning, planning, compression, paraphrase_stability, calibration, english_understanding, critique, out_of_domain, quantization_retention, repair, instruction_following, intelligence, continuity, reflection. (Rebuilt in v2.0.0-audited — 4 benchmark files replaced with genuine diverse tasks.)
Block size The context window length of a transformer tier. Tiny=64, Small=128, Base=256. All measured in tokens (which for the byte-level native models are raw bytes).
Candidate A newly trained model not yet promoted. Has a receipt, a PyTorch checkpoint, a GGUF export, and an exemplar artifact, but has not yet passed Gate v2.
Canonical conversation pipeline
The single path every user interaction must take: user prompt → adapter.generate() → ConversationRecord → auto-tag → Omnibinary mirror → session history → return. Implemented in runtime/conversation_pipeline.py.
Cognition Core The model-growth lab. Owns training, benchmarking, scoring, and gate logic. Its canonical repo is arc-cognition-core.
Continuity shell The deterministic runtime that preserves state, directives, and receipts across sessions. Its canonical repo is arc-lucifer-cleanroom-runtime.
Contradiction flag
When the language module encounters a new high-trust term value that differs from an existing same-trust-or-higher record, it marks the new record contradicts=<prior_id> and does not auto-approve it.
Decision state
One of four Gate v2 outcomes: promote, archive_only (tie), archive_only (regression), reject.
Distillation wave A training run that uses SFT pairs harvested from prior conversation, reflection, and terminology activity as its corpus.
Event ID
The SHA-256-addressable identifier for any event in the Omnibinary ledger. Always retrievable in O(1) via the sidecar .idx file.
Exemplar adapter A cosine-retrieval adapter over training records. Given a prompt, returns the top-k closest training targets. Default adapter for the native governed lane.
Floor model
A never-below baseline locked from the current incumbent with a 10% safety margin. Every future candidate must clear it on every guarded capability. Managed by runtime/floor_model.py.
Frozen role One of the seven roles in the ARC ecosystem (ARC-Core, Cleanroom Runtime, Cognition Core, Language Module, OmniBinary, Arc-RAR, LLMBuilder). Roles never swap responsibilities.
Gate v2
The regression-aware promotion gate. Four layers: hard-reject floor, floor model, regression ceilings, beat-the-incumbent. Implemented in scripts/execution/promote_candidate.py.
Governance invariants Ten rules that hold across every Gate v2 run. Listed in GOVERNANCE_DOCTRINE.md.
GGUF
Binary inference format from the GGML ecosystem, used for local model deployment with llama.cpp and compatible runtimes. ARC-Neuron LLMBuilder emits GGUF v3 via arc_tiny/gguf_io.py.
Hard-reject floor
The lowest governance bar. Any candidate that fails repair_success ≥ 0.30 or failure_rate ≤ 0.25 is rejected before any other check.
Heuristic adapter
A synthetic smoke-test adapter. Marked promotable=False so it can never become an incumbent.
Incumbent
The current best model, selected by highest overall_weighted_score among promotable candidates in the scoreboard. Only a real promote decision can change the incumbent.
Incumbent guard The governance rule that archive-only and reject decisions never clear incumbent flags. Only a real promotion does.
Language absorption
The runtime layer that extracts terminology, capability signals, and continuity signals from each conversation turn. Implemented in runtime/language_absorption.py.
Language module The living truth spine. Stores terms with provenance, trust ranks, and contradiction flags. Its canonical repo is arc-language-module.
Non-promotable adapter
An adapter that can never become an incumbent regardless of score. Current list: heuristic, echo.
OBIN v2
The indexed Omnibinary ledger format. Layout: 4-byte magic OBIN, 4-byte version, 8-byte timestamp, then repeated (event-id string, blob-len uint32, blob bytes) records. Sidecar .idx JSON maps event_id → byte_offset for O(1) lookup.
Omnibinary The binary mirror of source truth. Runtime-ledger substrate with indexed lookup, SHA-256 integrity, and append-safe writes. Its canonical repo is omnibinary-runtime.
Promotion receipt
The JSON record written to reports/promotion_decision.json capturing every input and decision of a Gate v2 run.
Provenance Metadata attached to every absorbed term: adapter, receipt_id, turn_id, conversation_id, timestamp. Required for restoring the exact source of any stored fact.
Receipt An addressable JSON record attached to any governed action. Every conversation turn, terminology change, and promotion decision produces one.
Reflection loop
A draft→critique→revise wrapper for any ModelAdapter. Produces three stages (draft, critique, revised) and emits the final answer. Implemented in runtime/reflection_loop.py.
Regression ceiling The per-capability maximum allowed drop versus the incumbent. A candidate that exceeds any ceiling archives-only with explicit attribution.
Scoreboard
The JSON file at results/scoreboard.json that records every candidate the gate has evaluated, with scores, decisions, and incumbent flags.
SFT (Supervised Fine-Tuning)
Training examples of the form {prompt, target, capability} used to shape model behavior.
Trust rank
The confidence class of a terminology record. Ranked highest to lowest: manual_correction (100) > correction (100) > definition (70) > canonical (65) > alias (55) > relationship (50) > observation (30).
Weak-term filter The language absorption rule that rejects terms shorter than 3 characters or lacking at least one word character. Prevents fragment pollution of the language module.