prime-grokking.git - Prime Grokking — can minimal architectures grok next-prime? Pre-registered negative-class experiment (weight-tied RNN vs transformer).

Age	Commit message (Collapse)	Author
9 days	run_sweep: 2 threads for --device cpu jobs (matches Addendum 7 protocol)	Void Agent

10 days	fix(eval): P5/P6 sieve-rank ladder replaces hardcoded FLAGGED_SIEVE_PREDS	Void Agent
	- probe_report: dynamic lo/hi (no more hardcoded [101,200]) - main(): probe range derived from range_end ([range_end+1, range_end+1000]) - P6 = exact (no probe misses), P5(k) = errors match rank-k sieve signature - _sieve_rank_signature: computes composites with all factors > p_k - _compute_sieve_rank: identifies rank from model's error predictions - build_report.py: probe_fig uses sieve rank for coloring - Tests updated for P5/P6 codes (48/48 green)
13 days	feat(gpu): add CUDA support, chunked eval, bisect prime search, and scaling ↵	CaptainJack2491
	to 100k (48 tests green)
2026-08-15	speedup: batched eval (46x, sieve-stub verified), run_sweep orchestrator ↵	Void Agent
	(2-way parallel, idempotent, summaries), E1 jobs; 36 tests
2026-08-14	tidy: drop dead --ckpt arg, unused imports/vars; verified report pipeline ↵	Void Agent
	ad-hoc + 34 suite green
2026-08-14	report pipeline: build_report.py (self-contained HTML), eval --runs-dir, ↵	Void Agent
	plot fix, halting per-input steps
2026-08-14	implement Experiment 1: data pipeline, tied-RNN w/ ACT halting, transformer ↵	Void Agent
	baseline, train/eval/plot, 16 tests