AURA v0.2.0 — calibrated, certified confidence (P0→P2)

AURA (Adaptive Unified Radiance Asset) v0.2.0 — the first tagged release.

AURA turns posed captures into a typed, queryable, engine-ready radiance asset. It
keeps the fast Gaussian / DBS-Beta renderers where they are strong and adds the
asset layer they do not provide: a calibrated, certified per-carrier confidence
that travels with the asset. This release consolidates the P0→P2 development arc
that hardened that killer property, plus a full presentation pass.

This is a research preview with an explicit honest boundary: every claim is backed
by a committed artifact, negatives are kept, and there is no official-leaderboard
SOTA claim anywhere in this repo.

Highlights — calibration + certificate hardened

• P0 killer property. Per carrier, a calibrated confidence c ∈ [0,1] (carriers
  reported at confidence p are reliable ≈ p of the time) plus a distribution-free
  split-conformal pruning certificate: drop everything below τ, losing at most ε
  reliability mass at confidence 1−α. Validated on four real scenes (Truck +
  Mip-NeRF 360 Garden/Kitchen/Room). Isotonic calibration drops ECE ~300–900×;
  calibrated-confidence pruning lands within 1–4% of the oracle ceiling and beats
  opacity at every budget (opacity sits at or below random). Survives an
  occlusion-aware reliability label. The value ships as _AURA_CONFIDENCE in the
  KHR_gaussian_splatting GLB and as a confidence vendor channel in the OpenUSD
  26.03 splat schema.
• P1 cross-scene transfer. A single calibrator transfers across scenes:
  selection/pruning AUC transfers within ±0.0004 on all 24 off-diagonal scene pairs
  (rank-invariant), absolute calibration ECE degrades gracefully (transferred ~0.008
  colour / ~0.026 depth on average — still 1–2 orders below the uncalibrated ~0.54),
  and the certificate stays valid when a small local conformal split is kept on the
  target. Deployment recipe: ship one calibrator + a small per-scene conformal set.
  The certificate's selective regime is mapped on all four scenes × both labels
  (onset ε* 0.47–0.62).
• P2 full resolution + a render-loss label. The P0 story holds at full resolution
  (full-res is at or slightly above the 0.25 control on every scene — not a low-res
  artifact) and survives a render-grounded reliability label measured from the actual
  alpha-composited render via exact blend-weight attribution, with honestly weaker
  margins (the export-time feature predicts the render-loss label at r ≈ 0.66–0.81 vs
  the proxy's 0.92–0.98; the oracle gap widens to ~6–13%; calibrated confidence still
  beats opacity on every scene).
• Presentation. README rewritten front-to-back with four result figures that
  regenerate from committed data (experiments/make_hardening_figures.py): the
  reliability diagram (raw vs calibrated), selection curves vs oracle/opacity, the
  cross-scene transfer ECE heatmap, and the colour-proxy vs render-loss comparison —
  plus the certified-pruning sweep animation.

The honest correction

P2 fixes a P0 evaluation leak: P0 trained on all frames, so its "held-out"
reliability views had been seen in training. On the clean, genuinely held-out split
the absolute numbers are slightly lower and honest — the Truck colour pruning
certificate that P0 reported as keeping 100% of carriers at ε=0.6 certifies keeping
77% on the clean split (1.00 → 0.77). All conclusions are stated relative to each
run's own oracle ceiling and to opacity, which is what transfers. The typed-carrier
Beta quality result (+0.33 dB on Truck, mean +0.80 dB) remains labelled as a
reproduction of Deformable Beta Splatting (arXiv:2501.18630) against a frozen-β
control — not an AURA novelty.

Unchanged

• The public Python/CLI API.
• The .aura package format + carriers.npz sidecar.
• The KHR_gaussian_splatting GLB and OpenUSD 26.03 exports (the calibrated
  confidence rides the existing vendor channels).

This is an additive, backward-compatible release: v0.2.0 hardens and documents the
P0 confidence property; it does not change how assets are built, packaged, or
exported.

Open items

• Garden native render-loss label needs an idle GPU. Garden's 17.4 MP
  rasterization OOMs on a loaded machine, so P2 renders that one label at half
  resolution (the carriers themselves are trained at native full resolution). A
  native full-res render-loss pass is pending an idle GPU.
• P3 independent re-captures. The reliability story is validated on four scenes
  and two labels; independent scenes / re-captures would harden it further.

Full per-change history: CHANGELOG.md. Deep-dives: docs/P0_CALIBRATED_CONFIDENCE.md,
docs/P1_CROSS_SCENE.md, docs/P2_FULLRES_RENDERLOSS.md. Licensed MIT.