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Deployed gap-injection run closed by operator steer 2026-07-04: R30 graduates on offline-tests + human face-validity, no deployed harness build, no endpoint. Harness (read/predict/record; write side stubbed) + brokkr's R30.10 protocol pin (de8357f) banked as drop-in for the parked powered true-tau perceptual study. predict() self-validated against brokkr's N=0 anchors (1h/10h/1wk).
65 lines
3.1 KiB
Markdown
65 lines
3.1 KiB
Markdown
# R30 gap-injection — deployed-run harness (BANKED, not run)
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**Status:** CLOSED — not executed. On 2026-07-04 the operator steered **close R30 on
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offline-tests + human face-validity**, no deployed harness build, no endpoint. No
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`(agent, N, Δt, observed p/a/d)` series was collected. This harness + brokkr's protocol
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are **banked / drop-in** for the parked powered true-τ perceptual study if it is ever
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commissioned.
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**Spec pin:** brokkr R30.10 protocol `de8357f` —
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`brokkr-smithy/research/R30-ocean-derived-mood-dynamics/empirics/r30-gap-injection-protocol.md`.
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## Why it was closed (not built)
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The deployed gap-injection run was **confirmatory, not measuring** (brokkr's §0 reframe:
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against a deployed system the fade is `exp(−Δt/τ_shipped)` by construction, so a fit
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returns τ_shipped tautologically — the run graduates the interim coefficients, it does not
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measure them). Given that:
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- The repo's **offline tests already cover the OU formula + BOTH directions**
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(`high_N_fades_slower_than_low_N` decay, `phenotype_high_n_bigger_negative_excursion` gain).
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- **b17 is deployed-clean** on demo + personal.
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- The ①-approved **mood-holds-within-conversation** IS the face-validity call.
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…the interim coefficients **graduate validated-as-shipped** with no deployed run needed.
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The build cost that would have been required (and was declined):
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- No deployed write affordance exists; the in-memory `_user_moods` cache **shadows** raw
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`persona_mood.db` writes, so a clean inject needs an in-service **set-mood/back-date
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endpoint** (set p/a/d + `updated_at` + evict cache).
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- `moody-lofn N=+0.8` **does not exist** (only lofn `N=−0.5`; tier-3 empty OCEAN → N=0), so
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the gain half would have needed a new high-N tier-1 agent + deploy — dropped as the
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expensive, offline-redundant half.
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## What is validated (harness self-check)
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`harness.py::predict()` reproduces brokkr's stated N=0 P-axis retention anchors **exactly**:
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| Δt | predicted | brokkr stated |
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|---|---|---|
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| 1h (3600s) | 0.904837 | 0.905 |
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| 10h/overnight (36000s) | 0.367879 | 0.368 |
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| 1 week (604800s) | 5.06e-08 | 5e-08 |
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Per-axis τ confirms arousal fades ~1.9× faster (N=0: τ_P = τ_D = 10h, τ_A = 5.26h).
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## Personal `:8081` b17 baseline survey (candidate N-grid agents, all at rest)
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| agent | baseline p/a/d | note |
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|---|---|---|
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| lofn | 0.809 / −0.153 / 0.248 | production, N=−0.5 |
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| mask | 0.0 / 0.0 / 0.0 | zero baseline → zero-N candidate |
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| forseti | 0.239 / −0.696 / 0.095 | production |
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| mimir | 0.615 / −0.438 / 0.304 | production |
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(sindra 404s — owner-scoped tier-3, expected.)
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## To un-bank (if the powered study is commissioned)
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1. Worldtree builds the in-service **set-mood/back-date endpoint** (set p/a/d + `updated_at`
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+ evict `_user_moods`), or the read-only fade-preview variant if that satisfies D1.
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2. Wire `harness.py::freeze_start()` + `backdate()` to that endpoint (near-zero rebuild).
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3. Run the 3×8 grid (frozen start p=−0.6/a=+0.5/d=−0.3 × Δt grid), hand brokkr the
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`(agent, N, Δt, observed p/a/d)` table + per-agent `baseline_pad()`; he runs D1/D2/D3.
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