Cipher Self-Healing SOP

A runbook, not a research paper. Every step is verifiable.

What "self-healing" means here

Cipher is a static GGUF plus a fluid QLoRA adapter plus a growing [learned] context block. The model does not retrain itself live. What it does:

1. Context healing — every approval/rejection feeds a JSONL bus (.aide/cipher-state.jsonl). High-frequency patterns (>=3 occurrences,

   =60% approved) are reformatted as [learned] lines and injected into the system scaffold on the next chat. The model gets better at your workflow without any weight change.

2. Sandbox self-correction — TASK proposals (file edits + commands) run against scratch copies first. Test/lint/compile results feed back into the model for up to 3 retries. Only verified diffs reach the SHIP panel. (Stage 2, not yet wired.)

3. Sleep-time weight healing — when idle, AIDE re-runs QLoRA over verified-good trajectories. The new adapter must pass the non-regression battery (delta >= 0) or it's archived. Operator approves the changelog before any swap. (Stage 3, not yet wired.)

4. Per-failure targeted healing — every category-FAIL in the capability audit becomes a curated SFT pair. Math FAIL -> math SFT pair. Reasoning FAIL -> enumeration SFT pair. (Stage 4, not yet wired.)

The state bus (.aide/cipher-state.jsonl)

One JSON object per line. Append-only. Read by every component that needs to know what the operator has done.

Event types in use today:

type	when	fields
approval	operator clicks Approve in a tool prompt	tool, pattern, summary
rejection	operator clicks Reject	tool, pattern, summary
abort	operator clicks Abort on a session	tool, pattern, summary
ship	a commit lands	commit_sha, files_count, intent
gate	any veritas gate runs	gate, passed, duration_ms
phase	model/engine phase change	from, to, engine
preference	explicit pattern write	pattern, direction
error	a component throws	source, message

Code: harness/cipher-state.mjs. Exports createStateBus(workspace) returning { append, readState, getPreferences }. Type declarations: harness/cipher-state.d.mts.

Failure mode: append is fire-and-forget — if the file write fails, no error bubbles. The bus is the training data source, not the audit log. Audit logs go to daemon/egress-journal.mjs and harness/events.mjs.

The [learned] block

Composed by createStateBus.getPreferences(minCount=3, limit=15):

1. Read last 500 approvals + 500 rejections
2. Group by pattern field
3. Keep only patterns with count >= 3 AND approved/count >= 0.6
4. Sort by approval count desc
5. Take top 15
6. Emit [learned] <pattern> lines

Why the 3-occurrence / 60% thresholds: prevents one-off flukes from poisoning the model. Pattern needs evidence.

Why cap at 15: the compact scaffold budget is 24 lines / 640 bytes (micro tier). The learned block should never displace core doctrine.

Wired where: node/src/routes/chat.ts in routeForChat (line ~123) calls getPreferences(3, 10) and appends the result to the system slot, after memory blocks, before the [workspace context] block.

The verification battery (non-regression gate)

Before any QLoRA cycle's adapter is promoted:

1. Run node scripts/run-arch.mjs against the current production adapter. Record composite per category.
2. Run the same battery against the candidate adapter.
3. Compute delta = candidate - production per category AND composite.
4. Gate: every category delta must be >= -0.05 AND the composite delta must be >= 0. Else archive with reason.

The battery covers: agent loop, agent tools, agent policy, agent contracts, byok, command registry, keybindings, settings, lsp/dap routes, training gate, exports, handoff, memory, orchestrator, workbench, plugin manager. See scripts/run-arch.mjs for the canonical serialized run.

The sleep-time cycle (operator approval-gated)

IDLE 2h
  |
1. Load last 24h of cipher-state.jsonl where type IN {approval, ship} AND
   any related gate.passed === true
  |
2. Build {prompt, verified_response} pairs from chat transcripts
  |
3. Mix 30% general instruction data (replay buffer)
  |
4. QLoRA fine-tune: rank 8, alpha 16, lr 5e-4, 2 epochs
  |
5. Convert to GGUF adapter via convert_lora_to_gguf.py
  |
6. Run verification battery against new adapter
  |
7. IF composite delta >= 0 AND every category delta >= -0.05
   -> stage adapter alongside current
   -> publish changelog to operator
   -> operator: [APPLY] [REVIEW] [SKIP]
   IF gate fails -> archive with reason, do not stage
  |
8. APPLY = hot-swap via /lora-adapters endpoint (<20ms)
   SKIP = keep current, try again tomorrow

The operator changelog

A human-readable summary AIDE produces at the end of a sleep-time cycle:

Overnight I learned:
- 3 new approval patterns (now seen >=3 times)
- 1 trajectory pair added to my training data
- Battery delta: +0.04 composite, all categories >= -0.02

[APPLY]  [REVIEW]  [SKIP]

The operator has VETO. Nothing is ever auto-applied.

Verification (per stage)

Stage	What to verify	Command
1 (state bus + [learned])	cipher-state.jsonl exists after one approval; [learned] block present in next chat	tail .aide/cipher-state.jsonl, then a chat + check system slot
2 (sandbox loop)	A TASK with a failing test retries up to 3x; verified-only diff reaches SHIP	manually test
3 (sleep-time)	First QLoRA cycle produces an adapter; battery gate holds; changelog appears	run cycle, inspect artifacts/
4 (per-failure SFT)	A category-FAIL audit entry produces a curated SFT pair in the next dataset	inspect data/

Threats and controls

Threat	Control
Garbage-in flywheel	Only verified outcomes (gate passed + operator approved) enter the dataset
Operator forgets to approve	Changelog persists until acknowledged; "stale" badge in cockpit
Battery regresses silently	delta >= 0 is a build-time gate in npm run check:arch
Adapter/base mismatch	intermediate_size compatibility check before --lora load
Catastrophic forgetting	30% replay buffer in every cycle
Privacy (training data = source code)	Local-only, no cloud, no telemetry, purge command in cockpit
VRAM contention	Train during idle hours only (P7 one-job law)

What this skill is NOT

• It is not the doctrine of what Cipher is. See aide-cipher and aide-cipher-house-model for the vision + lifecycle.
• It is not the build spec for the state bus. See aide-cipher-living-system for the spec the code was built from.
• It is not a QLoRA tutorial. See aide-cipher-house-model for the training config + conversion pipeline.