Self-Improvement Implementation Plan

Status: active build plan. Captured 2026-06-17. Companion to self-improvement-architecture.md (L2 now / L3 north-star, non-blocking) and extensibility-architecture.md (child-run keystone, merged v0.17.0). Each step = ~1 PR + 1 ADR section. All policy knobs configurable (Directive 1).

Where we start and why

The child-run substrate (v0.17.0) is the keystone. Step 1 is best-of-N fan-out on top of it, because that single primitive is also the lesson-quality engine and the paired-eval engine the L2 loop needs — so it is the shortest path that serves both the missing base primitive AND self-improvement.

Steps

Step 1 — Best-of-N fan-out on child-run (PR, v0.18.0) ← BUILD NOW

Run K child-runs of the same task concurrently (reusing child_run), collect their verified results, apply a Reducer. Reducers: Vote, JudgePanel (reuse delegation reviewers), BestScore, FirstSuccess. New events fanout_started / fanout_completed (per-child refs + which child won + why); ledger records the reduction decision and anchors each child. Hermetic tests. Explicitly leave as follow-up seams: the model-facing spawn tool, and refactoring the existing N-reviewer panel onto fan-out.

Step 2 — Spawn tool + panel refactor (PR, v0.19.0)

Expose child_run as a model-facing spawn tool (model can delegate a scoped sub-task and get a verified result back). Refactor the existing N-reviewer panel to be a JudgePanel fan-out instance (dedup the one-off). No new trust model.

Step 3 — SI-1 lesson extraction (PR)

At contract close, after a verified verdict, a step proposes generalizable lessons → EntryKind::Lesson written Unverified with provenance (LedgerRef to the producing run). Policy: extraction = off | on | model-scored. Also start the L3 seam here: introduce a typed/versioned/signed Policy governed-write object shape (same store/provenance as memory) — no optimizer.

Step 4 — SI-2 lesson promotion (PR)

Route lessons through the N-reviewer panel (delegation::Verdict) to gate Unverified→Verified against evidence. Reversible: regression supersedes/demotes. Reuses M3 trust-tier machinery end-to-end.

Step 5 — SI-3 lesson recall (PR)

Promoted lessons feed the M4 layered L2 injection. Verified-only by default; salience + scope are the already-enumerated knobs.

Step 6 — SI-4 paired eval harness (PR)

Run the same contract learning-on vs learning-off via child-run/fan-out; compare verdict pass-rate + cost; emit a report. Proves L2 helps; becomes the signal source an L3 optimizer would later optimize against.

L3 (north star — NOT built in this plan)

Once L2 is proven by SI-4: add a policy optimizer (dumb bandit/grid over enumerated knobs → later a small local tuning model) that proposes policy deltas through the SAME panel + provenance + reversible gate. l0_core / operator boundaries never auto-tunable; high-stakes deltas can require a human gate; never L4 (self-rewriting code).

Gate discipline (every step)

cargo fmt --check · cargo clippy --all-targets --all-features -- -D warnings · cargo test · cargo test --all-features · cargo build --release. ALINA re-runs gates on merge; PRs left open for review; ALINA tags on merge.