AURA v1.0.0

[1.0.0] — 2026-07-05

Scoped v1.0.0 release with documented limitations. The calibrated-confidence trust
layer — the load-bearing contribution — is complete and honestly bounded; the items the
release does not close (a full 8-scene true-3DGS control, external reproduction, the
UBS-6D arm, and the demo/metadata carriers) are documented as open, not implied done,
in the README's "v1.0 Known Limitations" section. This release folds the B2 true
gsplat-3DGS control result and freezes the P0→P2 + CPU-ladder work into a citable version.

Added (v1.0.0)

• B2 — true gsplat-3DGS MCMC control (Truck). A genuine gsplat-3DGS control
  (simple_trainer.py mcmc, cap_max=1e6, 30k steps, every-8th-view split) at a matched
  1M-carrier budget on Truck, replacing the frozen-β DBS ablation for that one scene. Result
  (outputs/gsplat_control.json, now committed via git add -f): true gsplat-3DGS 25.94 dB
  (final@30k) vs frozen-β control 25.96 vs adaptive Beta 26.39 — the typed-carrier win
  holds against real 3DGS (+0.45 dB), and the frozen-β control lands within 0.03 dB of
  true 3DGS, so it was not artificially weak. Honest bound: Truck only (1/8 scenes); the
  other seven scenes and the +0.80 dB 8-scene mean remain frozen-control numbers, and UBS-6D
  was not built. New reproducible figure assets/b2_gsplat_control_truck.png
  (experiments/make_b2_gsplat_control_figure.py, reads the JSON verbatim).
• Version bump 0.7.0.dev0 → 1.0.0 (pyproject.toml, src/aura/__init__.py, README,
  paper); PyPI development-status classifier Alpha → Beta (honest for a documented-limitations
  preview release).
• README "Road to v1.0" rewritten to "v1.0 Known Limitations"; the "gsplat-control"
  naming collision resolved — the frozen-β/fixed-Gaussian control (8/8 scenes) is now named
  distinctly from the true gsplat-3DGS MCMC control (Truck, 1/8).

Added (v0.3→v0.7 CPU ladder, landed 2026-07-03)

• Certified LOD / streaming (src/aura/lod.py, aura lod-plan, docs/P4_CERTIFIED_LOD.md):
  carriers stream in descending calibrated confidence with K published stopping
  levels, each carrying a distribution-free bound on discarded reliability mass at
  Bonferroni α/K (family-wise 1−α). All 16 bounds hold on disjoint eval halves
  (outputs/lod_certified.json). Finding: isotonic plateaus make τ-rounding unsafe —
  τ is stored at full precision.
• SPZ v4 export (src/aura/spz.py, aura export-spz): pure-numpy NGSP
  reader/writer cross-validated bit-exact against the reference C++
  (nianticlabs/spz @ bb0efad; harness preserved at
  experiments/spz_reference_crossval.cc); confidence rides as a
  .spz.confidence.npz sidecar (v4 has no per-splat channel).
• BVH batched ray query (src/aura/bvh.py, docs/P5_BVH_RAY_QUERY.md):
  median-split BVH whose leaf AABBs provably superset the isotropic hit test ⇒
  exact parity with brute force (0 mismatches incl. 300 rays on the real truck
  asset); batched API + build-once streaming handle; 0.39% node visits / 7.2%
  carriers per ray on the truck.
• Carrier maturity contract (carriers.py, gate carrier_registry_honesty):
  every carrier type declares trained / demo / metadata; a trained claim
  requires committed calib_<scene>.json evidence. Hybrid neural routing is now an
  explicit provenance-annotated Gaussian fallback (fallback:gaussian +
  RuntimeWarning), never silent; prism.make_neural_footprint is quarantined
  behind enable_experimental=True.
• Codebook semantics (src/aura/codebook.py, docs/P6_CARRIER_REGISTRY_AND_CODEBOOK.md):
  K-entry k-means codebook + uint8/16 per-carrier indices; O(K·d + N)
  open-vocabulary fan-out; real truck DINOv2 features compress 1.53 GB → 1.05 MB at
  k=64 (recon rel-err 0.319). Feature distillation into the shipped asset stays
  GPU-gated.
• Publication gates content-checked (publication.py): 11 existence checks →
  17 gates that parse committed artifacts and enforce numeric thresholds;
  trained-asset probes return explicit unverified/requires_gpu instead of
  passing. Split guard (split_guard.py) makes the historical P0 eval-leak
  class mechanically impossible (including the Truck-certificate-back-at-1.00
  fingerprint).
• CI (.github/workflows/ci.yml): CPU suite on Python 3.11/3.12 on every
  push/PR; gpu / local_data pytest markers.
• REPRODUCE.md: verified, CPU-only, bit-for-bit reproduction of the
  calibration / certificate / LOD results from a fresh clone (the
  reliability_*.npz inputs are now committed, ~23 MB).
• Relight decision protocol (docs/P7_RELIGHT_DECISION.md,
  experiments/relight_benchmark_harness.py): pre-registered promote-or-descope
  rule for the v0.8 inverse-rendering attempt; TensoIR/Stanford-ORB harness with a
  CI-tested smoke mode. Relight module docstring corrected to preview-stage.
• USD confidence primvar: custom:aura:confidence → idiomatic
  primvars:aura:confidence (vertex interpolation) with a legacy fallback reader.
• AURA preprint updated to this state (17 pp; publishes at v1.0, owner decision).

Known limitations at v1.0 (documented open, not closed)

• B2 true gsplat-3DGS control is Truck-only (1/8); the 8-scene mean stays a
  frozen-control number; UBS-6D arm not built.
• Garden native 17.4 MP render-loss label rendered at half resolution (OOMs under
  concurrent GPU load); v0.7b gabor real-training attempt not landed (registry stays scoped
  to two trained carriers); v0.8 relighting stays a preview by its pre-registered
  promote-or-descope rule (not attempted at bar).
• Ray query is a CPU-BVH parity result, not a GPU wall-clock match to 3DGRT/3DGUT.
• No external reproduction; no P3 independent re-captures (four single-capture scenes).
• Established honest negatives (kept, not defects): adaptive per-carrier β does not beat
  a good global β; cross-family mix-routing never beats the best single family.
  See the README "v1.0 Known Limitations" section for the full list.