Campaign primitives - #2
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…ty + Caesar-crib diagnostic Tests whether a periodic cipher's cosets are homogeneous (one type) or a mixture, via an IoC-variance homogeneity p-value plus a per-coset best-Caesar LLR that flags cleanly-keyed cosets as exploitable cribs. Uses per-coset multisets only, so it is invariant to any within-coset transposition. Pure-stdlib; complements classify_coset_ioc (which classifies the mean). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Consolidation of techniques that were hand-rolled across the PK working directory and independently re-derived by multiple community solvers. The general, PK-agnostic core of each is now a first-class, tested module. - scoring.BatchNgramScorer: vectorized 26**n LUT batch scorer that reproduces NgramScorer.score exactly; optional numpy accelerator with pure-Python fallback. get_batch_scorer() caches one over the bundled tables. - nulls (new): structure-preserving null models (within_coset, block_shuffle, permutation, iid_*) + null_test / scan_test with look-elsewhere correction. The honest max-over-search null falls out when the statistic maxes internally. - ciphers/keystream (new): self-generating additive keystream family — keystream/lfsr (linear recurrence) and lcg — with vigenere/beaufort/variant combiners; blind cracks brute the tiny keyspaces via seed-linearization matmul and the batch scorer. Registered in the cipher registry. - construction (new): forward-simulation model-ID — simulate each family, measure a dial vector, refute families that cannot manufacture the observed dials. Complements the discriminant-based cipher_id/diagnose. - power (new): objective power calibration — separation (z/d/AUC), powerless_objective (flat/key-invariant detector), power_curve + dilute_plaintext. - wordlm (new): word_segment (dictionary tiling; long_coverage catches n-gram salad) + word_tiling (reconstruct plaintext from letter multisets; tiling count is a determinacy signal). 46 new tests; ruff + mypy clean; full fast suite green; leak sweep clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Run the formatter over the branch's changed files. No behavior change: reflows only. Covers the new modules/tests from this branch plus the coset_homogeneity diagnostic's analysis.py/test_analysis.py, which were committed before the formatter was run. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The within-coset and block-shuffle nulls preserve each coset's letter multiset exactly, so coset-IoC at the period is mathematically invariant. The derived float can still jitter at ~1e-15 (platform-dependent libm summation — seen on the CI Python 3.10.20 leg, exactly 0 locally), which broke bit-exact `== 0.0` / `== null_mean` / `== 1.0` assertions. Assert the invariance with tolerance instead, and check the block-shuffle property at the integer multiset level (sorted coset == sorted coset) rather than through the derived float. Behavior of nulls.py is unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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