Learning Datagraph — SLM trajectory loop and apprenticeship queue

The platform builds a compounding substrate: every operator interaction with an AI session becomes a structured training tuple, routed through a single auditable boundary (Doorman), captured to an append-only ledger, and folded back into the local SLM via periodic fine-tuning. The result is a development environment that learns from how it gets used — code completions improve toward the patterns this operator writes, draft suggestions align closer to the editorial voice this house produces, entity extractions tighten as the graph thickens.

[edit]Key Takeaways

• The substrate accumulates training signal through four distinct legs: trajectory capture at session end, an apprenticeship queue that fires on every commit, editorial DPO pairs from the reverse-funnel editorial pipeline, and negative-trajectory distillation from operator corrections. Each leg captures a different dimension of operator intent.
• All training signal passes through the same auditable boundary — Doorman — and lands in the append-only audit ledger. Nothing bypasses the ledger; nothing leaves the local environment. The learning loop is air-gapped and self-contained.
• The corpus accumulates with every session. As of mid-2026 the apprenticeship corpus held 502 tuples and the editorial DPO corpus held 34 pairs. These numbers grow without manual curation — the model floor rises as the operator uses the environment.
• The one leg not yet wired is the structured-entity loop: a POST /v1/draft/generate endpoint in service-content that would ground generation in graph entities. The supporting infrastructure (queue, ledger, hooks, audit routing) is already in place; what remains is a multi-week Rust engineering effort.

The substrate has four legs.

Trajectory capture. A session-end hook fires at session close, writing a structured JSONL entry to the audit ledger: branch state, uncommitted-file count, head SHA, and a promotion-pending flag. A nightly harvest copies the day's session transcripts into the same ledger, tagged by operator and archive.

Apprenticeship queue. A post-commit hook emits a brief for every workspace commit. A 15-minute queue drainer calls the local SLM (OLMo-2 7B Q4) against each brief, captures the model's attempt, and writes the (brief, attempt, actual_diff) tuple to the apprenticeship corpus. 502 tuples had accumulated as of 2026-05-18.

Editorial DPO pairs. Every draft that passes through the reverse-funnel editorial pattern — raw to refined to creative-edited — emits two DPO (direct preference optimisation) pairs to the prose-edit corpus. The pair captures the editorial improvement deltas. 34 pairs had accumulated to that date.

Negative-trajectory distillation. An inbox-scanner script reads operator corrections from archived messages and emits negative-trajectory signals to the feedback corpus. This fourth leg captures what the model should not do.

What remains to wire — multi-week Rust engineering effort: the structured-entity loop. service-content (LadybugDB-backed graph) needs a POST /v1/draft/generate endpoint that queries the graph for relevant entities, assembles a 2K-token grounded prompt, calls the Doorman, and writes the response as a graph-grounded corpus tuple. A LoRA scheduler then wakes Tier B GPU compute for nightly adapter training. The supporting infrastructure — queue, ledger, hooks, audit-routing — is already in place.

The substrate compounds in two directions: structurally (citation density and supersedence chains thicken with each draft) and generatively (each adapter raises the floor of "raw" so each refinement cycle starts closer to publish-ready).

[edit]See also

• compounding-substrate — the substrate discipline this architecture instantiates
• service-slm — the local SLM service that executes model inference in the loop
• totebox-session — the session model that trajectory capture instruments at session end
• mailbox-atomicity — the atomic prepend discipline that protects the audit ledger from concurrent write races