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| # Verification — 1 November 2026 | |
| ## Verified locally | |
| - Apple Silicon, Python 3.13.18, PyTorch 2.15.0. | |
| - Metal GPU tensor operations and backward pass work outside the app's filesystem sandbox. | |
| - 10 CPU tests passed: Gaussian PLY round trip; mesh/point PLY rejection; coordinate bounds; | |
| deterministic cell selection; cache hash and overwrite protections; aligned geometry target import; | |
| missing-teacher masking; quaternion sign invariance; query-chunk attention equivalence; | |
| object-level split integrity; and measurable loss reduction during training. | |
| - 2 additional Metal tests passed: default 16³ student forward/backward; and the official Idol | |
| architecture's tiny randomly initialized denoiser/sampler, octree and Gaussian decoder. | |
| The tiny teacher uses the pinned upstream SDK with the real-valued RoPE / chunked attention adapter. | |
| ## End-to-end synthetic smoke test | |
| - 12 procedural ellipsoid image/splat pairs. **No teacher-generated samples.** | |
| - 10 training objects, 2 held-out validation objects; seed 42. | |
| - Student: grid 8, width 64, 306,321 parameters, FP32. | |
| - 100 AdamW updates on MPS, batch size 1, learning rate 0.001. | |
| - Held-out loss: 1.35705 at step 1 → 0.55299 at step 100. | |
| - Held-out occupancy IoU at step 100: 0.48258. | |
| - Timed training loop including periodic validation/checkpointing: 6.383 seconds. | |
| This tiny 8³ fixture test is not a runtime estimate for teacher inference or serious training. | |
| - Reloaded the saved checkpoint and generated a 40-Gaussian PLY for held-out fixture 007. | |
| - Inspected the four-view diagnostic PNG. It is a coarse, sparse synthetic result, not a realistic reconstruction. | |
| ## Not verified / not completed | |
| - No pretrained Idol weights or gated encoder weights were downloaded. | |
| - No complete real-image Idol inference on MPS; tiny component success does not establish full-size memory feasibility. | |
| - No InstantMesh or Hi3D inference on Mac. Their outputs can only be imported into the student dataset. | |
| - No training on real teacher outputs, real-image evaluation, teacher-quality matching, 1-bit weights, or binary kernels. | |
| - No multiview rendering loss, automatic cross-teacher alignment, or teacher disagreement filtering. | |
| At inspection time, only about 7.4 GB of disk was available before installing dependencies. | |
| The teacher downloader uses a conservative 16 GiB free-space guard for new downloads. | |
| The user still needs access to DINOv3 and FLUX.2-dev encoder repositories and actual object images/teacher data. | |
| The files in `examples/synthetic-smoke` are included for reproducibility, not as pretrained production assets. | |