Follow up final eval JSON types and cache-only model loading #157

At PR #12 head 17e338d, the final bot reviews identified two non-blocking follow-ups.

Keep pass flags boolean in JSON

experiments/evals/evals/map3d.py:169-172 can return numpy.bool_. The default=float JSON fallback serializes it as 1.0 or 0.0. The committed facing rows in results/map3d.json:53-79 contain 1.0 while other pass flags are booleans. The threshold calculation itself is unchanged; the defect is the inconsistent output type.

Return a native bool or use a NumPy-aware serializer. Add a strict type regression and update the saved JSON without changing the measured values.

Both bots reported this same defect:

• feat(evals): real-data evals for AR drift, wall reconstruction, coverage and the field test #12 (comment)
• feat(evals): real-data evals for AR drift, wall reconstruction, coverage and the field test #12 (comment)

Load the grouped model only for a cache miss

experiments/evals/models/run_groups.py:73-82 loads and verifies the model before the matching-input cache check at 96-99. A cache-only invocation still needs weights, can fail offline after the checkpoint was removed, and allocates the model without inference. This is unnecessary work, not evidence of uncontrolled memory growth or a wrong result.

Calculate candidate fingerprints from the pinned checkpoint identity first. Load and verify the actual checkpoint when inference is required, preserving cache validation and provenance checks. Test an all-cache-hit invocation with the model loader mocked to fail if called.

Review: #12 (comment)

Deferred under the manager's normal-use/plausible-failure convergence standard. The separate pose-realization and grouped-publication findings are not included in this issue.