# Runtime v0.0.2

This release optimizes inference code; checkpoint weights and model intelligence are unchanged.

Feature encoding tokenizes each goal once, reuses lexical scores for retrieval, and
constructs tensors once per batch instead of repeatedly assigning tiny tensors.
Policy conversion no longer recursively copies unused DOM fields.

`LearnedPolicy.predict_batch([(goal, state, ticket), ...])` supports at most 64
independent requests. Each returned decision retains its own ticket and observed
target references. Confidence abstention, sensitive-target exclusion, and value-copy
restrictions remain in effect. Empty batches return an empty list.

Measured on the development Windows CPU with two Torch threads and the mean
checkpoint: median single-request prediction 2.103 ms before / 0.884 ms after
(2.38x); p95 3.304 / 1.327 ms. A 16-request batch took 6.253 ms median, about
0.391 ms per request. These are synthetic CPU policy timings, not browser-task
latency or target Linux VPS results. Loading, network, and browser time are excluded.

All encoded inputs, targets, and candidate maps were bit-identical across 1,440
validation/test/novel-wording examples. Individual action/ticket parity was checked
on 160 requests; batch actions matched individual actions on those requests.
Novel-wording raw step match remains 55.83%; this release does not fix generalization.

Evidence: `reports/runtime-optimization.json`. Run `python -m unittest discover -s tests`
for runtime regression tests. The existing `baim.bench_policy` command can measure
the current feature encoder independently. Compare against Hub revision
`795f73703a1e33b82636b2167d7d9507581efd4d` for the prior implementation.