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
44 hours	optimize E8: precompute causal mask buffer, add eval early exit, and enable ↵	CaptainJack2491
	compile_model in e8.csv
44 hours	implement Addendum 8: 6-arm token-space recurrence suite (arms A, B, C, D1, ↵	CaptainJack2491
	D2, D3), tests, and jobs/e8.csv
11 days	fix(train): enforce epoch permutation without replacement, add max cache OOM ↵	CaptainJack2491
	guard, and handle empty split tensors
11 days	perf(train): VRAM-resident tensor caching, zero-copy GPU batch sampling, and ↵	CaptainJack2491
	adaptive eval schedule
2026-08-17	perf(cuda): add AMP fp16 autocast with GradScaler and torch.compile ↵	CaptainJack2491
	reduce-overhead mode
2026-08-17	feat(gpu): add CUDA support, chunked eval, bisect prime search, and scaling ↵	CaptainJack2491
	to 100k (48 tests green)
2026-08-16	phase3: task_mode (is_prime), train_frac, lr_decay flags + tests (41 green); ↵	Void Agent
	Addendum 5 locked; 11-job batch
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	fix codex BLOCKERs: global fixed layout invariance, min_steps off-by-one, ↵	Void Agent
	integers EOS alias, rerun guard, eval prereg-literal codes + dual-checkpoint honesty; +7 regression tests
2026-08-14	rnn: fully-tied cell (read-in+compute+decode), lambda ramp, ACT regression ↵	Void Agent
	test; design: prior art + review
2026-08-14	implement Experiment 1: data pipeline, tied-RNN w/ ACT halting, transformer ↵	Void Agent
	baseline, train/eval/plot, 16 tests