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Phases

24. Minimum Viable System

The first coherent implementation contains:

  • one host architecture;
  • one insertion region;
  • one reversible growth slot;
  • one universal residual growth envelope;
  • Tolaria ordinary host training through one deterministic step engine;
  • exact Tolaria snapshot, restore, branch and common-future replay under one Academy reference profile;
  • one explicit three-axis ScaffoldState and scaffold manifest registry;
  • a calibrated-stochastic harness capable of measuring Field-to-Academy disagreement, even if full Field operation remains disabled;
  • one fixed Tamiyo strategic envelope;
  • one heuristic Narset tactical controller;
  • one Nissa telemetry schema published directly to Narset and Momir;
  • one narrow Narset GrowthIntent and deterministic request resolver;
  • one versioned Sarpadian stock-reference bootstrap corpus;
  • one small deterministic or latent-conditioned Momir designer that also runs with ancestry absent;
  • one rule-driven Elesh verifier and canonicaliser;
  • one eager-mode Tezzeret compiler with explicit manifests;
  • one rule-driven Urabrask QA suite with mandatory no-op measurement;
  • one fixed, provider-blind Augustin judge;
  • fixed Kasmina maturation and blend schedules;
  • fixed Emrakul safe-maintenance rules;
  • Sarpadia retention of complete candidate pools, evidence and decisions;
  • Oona flight recording and branch inspection;
  • reference-seed bootstrap, scaffold-withdrawal, static and short-horizon counterfactual curricula;
  • and random, analytic, retrieval, online-optimised and no-op controls.

The MVP does not require:

  • learned Tamiyo;
  • learned Augustin;
  • learned Emrakul;
  • arbitrary graph generation;
  • asynchronous CUDA code generation;
  • diffusion;
  • multiple insertion regions;
  • or image-scale tasks.

25. Implementation Sequence

Phase A — Namespec, Leyline and dependency boundaries

  • record Namespec 1.0 in an ADR;
  • define package ownership and forbidden authority;
  • define all core contracts;
  • encode lifecycle and warrant rules;
  • implement budgets and spend records;
  • establish versioning and compatibility;
  • and add import-lint and authority tests.

Phase B — Tolaria host-training baseline and Academy profile

  • move ordinary host execution behind one Tolaria engine;
  • make data, optimiser, scheduler, precision and device state explicit;
  • define the Academy-exact runtime profile;
  • establish deterministic mainline traces;
  • implement ScaffoldManifest and ScaffoldState recording;
  • and ensure Kasmina exposes a neutral host-runtime protocol.

Phase C — Kasmina, Elesh and Tezzeret mechanics

  • implement the universal growth envelope;
  • define raw and canonical graph IRs;
  • implement structural validation and canonical hashing;
  • implement eager compilation and manifests;
  • and prove raw-to-canonical-to-artefact identity.

Phase D — Tolaria replay, branching and execution calibration

  • capture host, optimiser, lifecycle, controller, RNG, dataloader, task and future state;
  • implement Academy-exact restore and common-future replay;
  • implement branch adoption or validated replay;
  • pass the Academy determinism gate;
  • add repeated calibrated-stochastic branches;
  • measure ranking, decision and tail disagreement against Academy;
  • and keep Field profiles disabled until the declared gate passes.

Phase E — Urabrask QA

  • implement TestPlan;
  • implement runtime semantic and gradient conformance;
  • implement numerical, determinism and regression checks;
  • implement multi-horizon measurements;
  • produce signed QualityReport;
  • establish Academy versus Field QA;
  • and implement escalation from uncertain Field evidence to Academy-exact retest.

Phase F — Augustin adjudication and controls

  • implement hard eligibility;
  • implement mandatory no-op policy;
  • implement provider-blind utility and risk;
  • add screen and independent audit rules;
  • bind warrants to evidence;
  • add random, analytic, retrieval and bounded online controls;
  • and produce the QA-cost and adjudication-regret curves.

Phase G — Sarpadia and Momir bootstrap curriculum

  • migrate legacy stock blueprints into a versioned reference population outside Kasmina;
  • store complete pools, reports, decisions and no-op cases;
  • implement blinded views, lineage, equivalence and ancestry records;
  • collect frozen-state teachers, parents, mutations and failures;
  • train structural reconstruction and behavioural prediction;
  • train local mutation, recombination and ancestry dropout;
  • pass Momir's independent design-prior withdrawal gate;
  • retain stock seeds as blinded controls after ancestry withdrawal;
  • and consume losers through ranking or utility objectives.

Phase H — Nissa routing and Narset lifecycle integration

  • publish one Nissa observation directly to Narset and Momir;
  • replace blueprint actions with GrowthIntent;
  • implement deterministic GrowthRequest resolution;
  • train local commissioning and lifecycle behaviour with known-good candidates;
  • run anti-collusion and assignment-brief tests;
  • enable generated requests;
  • and add nursery re-qualification.

Phase I — Emrakul maintenance

  • implement continued-tenancy QA;
  • implement Augustin maintenance decisions;
  • calibrate sedation, decay and lysis execution;
  • and separate host dependence from intrinsic value.

Phase J — Tamiyo strategic allocation

  • introduce multiple regions or cells;
  • allocate global resources;
  • and train or search strategic policies after local behaviour is stable.

Phase K — Oona and scale

  • complete event projections and audit bundles;
  • expose architecture-smell events;
  • expand the grammar;
  • add image tasks;
  • and scale only after synthetic reliability gates pass.