claude-opus-5

Done. All three deliverables are in place and the final selection is the best-measured configuration.

Result: dev perplexity 350.27 vs 457.99 for the random baseline (−24%).

Method (submission/curate.py, one 40s CPU pass, no GPU):

1. Decode multi_dev.npy, split into its four equal register blocks (wiki / general web prose / news / technical Q&A) → four positive unigram+bigram distributions.
2. Score every pool doc by length-normalized interpolated log-likelihood ratio mean_t log(λ·p_d(t)/p_0(t) + 1−λ), λ=0.7, against a pool background p_0.
3. Gate boilerplate (length, non-ASCII/markup fraction, type-token repetition), dedupe near-duplicate page families on head/tail word sequences.
4. Assign each doc to its argmax register, greedily fill each register, emit token-balanced interleaved so every prefix of the list is an equal-parts mixture.

Ablations run (each a full frozen training run, recorded in claim.md): the add-1-smoothed variant of the same scorer scores 463 — worse than random, because unbounded per-token weights select vocabulary-novel nav-bar/link-farm pages; skewing the mixture (360), loosening the Q&A gate (365), and scarcity-first register ownership (359) all lost to the equal-parts argmax version.

Validity checks: 28,813 unique ids in range, packs to the full 12,000,000 tokens.