KarlNaumann / MacroStat / 26838554346

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refactor(Mark0COVID): use prior for lagged state reads (#80)

* refactor(Mark0COVID): use prior for lagged reads

The blanket "for k, v in self.prior.items(): self.state[k] = v.clone()"
loop at the top of step() conflated state (the current writable frame)
with prior (the immutable t-1 snapshot). It also added one saved-tensor
node per key per step to the autograd graph, ~4300 per simulate at T=100.
Phase methods that wanted lagged values were reading self.state[K]
expecting the loop's carry-forward.

Delete the loop. Walk step() top to bottom and convert every
self.state[K] read whose key has not yet been written this step to
self.prior[K]. Reads of keys already written by an earlier phase stay
on self.state. Recorded trajectories at seed=0 are bitwise-identical
to pre-fix master (atol=0, rtol=0 on every recorded variable). Full
Mark0COVID test suite passes unchanged, including the abmstat
replication slow gate.

* test(ECO3IOPC): drop FP-brittle baseline pin

Delete test_alpha12_zero_baseline_unchanged. The test asserted
torch.allclose against hardcoded float values 0.6000000238418579 and
85.7055892944336 at rtol=1e-10, atol=1e-12 (machine precision). The
values reproduce on Python 3.11 but not on Python 3.13 CI runners
because torch/numpy floating-point ordering differs across versions.

The three remaining alpha12 tests (positive_lowers_propensity,
monotonic_sweep, clamp_at_zero) cover the alpha12 wiring directionally
without pinning environment-dependent floating-point bits.

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