| --- |
| license: cc-by-4.0 |
| pretty_name: cascade held-out eval pool (lagged exact reveal) |
| tags: |
| - time-series |
| - forecasting |
| - benchmark |
| --- |
| |
| # cascade eval pool — lagged public reveal (exact bytes) |
|
|
| Retired snapshots of the **held-out evaluation pool** used by the |
| [cascade](https://github.com/TensorLink-AI/cascade) subnet. Each folder is a |
| **byte-identical mirror of the `pool/snapshots/block-<N>.tar` that validators |
| scored** — downloaded from the private pool bucket, sha256-verified against the |
| publisher index, and republished unmodified. A snapshot is revealed only after |
| a newer snapshot has superseded it, so no revealed pool can be selected by a |
| current or future round. |
|
|
| ## Verifying a round receipt |
|
|
| 1. Your receipt's pool provenance carries the snapshot tar's sha256. |
| 2. Find the folder whose `POOL_SHA256` matches; `block-<N>.tar` in that folder |
| is the exact artifact — hash it yourself to confirm. |
| 3. Series order = filenames sorted lexicographically; `window_ids` are |
| positional (`w<i>` = the i-th series in that order). |
|
|
| ## Layout |
|
|
| ``` |
| snapshots/<as_of>-block-<N>/ one folder per revealed snapshot |
| block-<N>.tar the EXACT tar validators scored |
| POOL_SHA256 its sha256 (matches receipts + publisher index) |
| <series_id>.npy the same files, unpacked for convenience |
| metadata.json {series_id: {freq, seasonal_period, domain, source}} |
| provenance.json build config recorded at publish time |
| ``` |
|
|
| Legacy `snapshots/<YYYY-MM-DD>/` folders (through 2026-08-03) predate this |
| scheme: they were **rebuilds**, not byte mirrors, and are known to differ from |
| the scored tars (see the repo issue history). Use the `-block-<N>` folders for |
| exact replay. |
|
|
| ## Latest revealed — `2026-08-08` (block 8798400) |
|
|
| - **series:** 1893 |
| - **sha256:** `97a289d0ea7ad9832cc790b6a1b3fca98718c7b94a2ef717b874b21cead025c1` |
|
|
| > This is an **evaluation** set, not training data. Publishing it to train on |
| > would contaminate the benchmark it exists to measure. |
|
|
