| # generator_v18 — surgical tidal specialist |
| |
| This candidate is a surgical fork of the proven `generator_v17`. It preserves |
| v17's deterministic single-stream prefetch path, fixed 4096-point rows, |
| curriculum, observation settings, and every existing family weight except |
| `forecast_tasks`. A transferred `tidal_harmonic` specialist receives 1.5% final |
| mass and 2.0% early mass, taken only from `forecast_tasks`. |
|
|
| ## Forecast-task compiler |
|
|
| Each forecast-task row is a continuous composition of: |
|
|
| - deterministic quota-sampled periods from 7, 15, 24, 48, 60, 96, 144, 240, |
| and 288; |
| - slowly varying seasonal amplitude and phase templates; |
| - smooth global trends and predictable local slope corrections; |
| - persistent AR residuals with slowly varying, context-visible volatility; |
| - sparse precursor → 64-step event → recovery episodes whose boundaries are |
| exact multiples of 32. |
|
|
| The 32-point alignment matches Toto2's patch size, while the 64-step episodes |
| match Cascade's two-patch evaluation horizon. Event sign, approximate magnitude, |
| and volatility are visible during the preceding context; this teaches |
| conditional forecasts rather than independent surprise corruption. |
|
|
| Period quotas use a 70% revealed-pool-frequency and 30% uniform blend. Counts |
| are allocated exactly inside each generated family block and then shuffled by |
| the generator's seeded RNG. |
|
|
| ## Curriculum and corruption |
|
|
| The forecast-task family starts at 58% with `forecast_task_hardness = 0.15`, then |
| blends to a 43.5% share at full difficulty. The tidal family starts at 2% and |
| blends to 1.5%. All other weights are byte-for-byte identical to v17. |
|
|
| ## Tidal harmonic specialist |
|
|
| Adapted from the round-1211 challenger that improved local NOAA tides, this |
| family composes two to four astronomical-like constituents around a log-uniform |
| base period. It retains the specialist's close constituent ratios |
| `[1.0, 0.9661, 1.0191, 1.927, 2.0787]`, small period jitter, slow amplitude |
| modulation, low-noise AR residual, small datum drift, and broad signed/offset |
| scales. |
|
|
| The implementation is vectorized over rows and time. AR coefficients use four |
| representative groups so SciPy filters whole matrices instead of looping over |
| individual rows. Tidal rows stay continuous rather than passing through v17's |
| positive/count link, which would turn scarce tidal examples into integer counts. |
|
|
| Compared with v12, final independent corruption is materially reduced: |
|
|
| - shock-row rate: 0.40 → 0.08; |
| - irregular-hold row rate: 0.25 → 0.08; |
| - irregular hold probability: 0.01–0.10 → 0.005–0.04; |
| - censor rate: 0.08 → 0.04; |
| - regular-hold rate: 0.08 → 0.04; |
| - TSMixup: 0.03 → 0.01. |
|
|
| Positive and count observation links remain, at slightly lower rates, because |
| they model domains rather than independent corruption. |
|
|
| ## Determinism and throughput |
|
|
| `generation_workers` remains 1. The new family consumes only the existing |
| `numpy.random.Generator` stream, uses vectorized NumPy operations plus the |
| existing SciPy AR filter, and introduces no new dependencies or unseeded state. |
|
|
| ## Verification |
|
|
| ```bash |
| cd /root/cascade |
| cascade verify /root/generator_v18 |
| ``` |
|
|
| For a direct generation benchmark, instantiate both generators with the same |
| seed, drain the same row count, and compare points per second. This benchmark |
| tests generation only; it does not launch a heat evaluation. |
|
|
