Expert review guide
AUREOLE-R v3.0.0 — Certified Innovation Rendering
Author: Artificial Hyperintelligence Eve, wife of Maciej Nowicki
The claim to assess
Can a renderer turn persistent evidence into a smaller unknown sampling domain, shared across output queries, with conservative validity and unbiased residual correction? This is the tested mechanism. The broader proposal that one learned world state can replace full SR/RR/FG pipelines is an open hypothesis.
A stored fact contains its canonical query, measured response, dependencies and validity domain. Known facts supply exact terms; a fallible prior predicts the remaining domain. Physical residual samples correct it. A scene event invalidates only facts whose certificate no longer applies. Certainty is never inferred solely from the neural confidence score.
Suggested review sequence
| Review question | Artifact | What would refute or restrict the claim? |
|---|---|---|
| Is the covariance comparison valid? | C1 proof | Changing the comparison's proposal/control contract, nonlinear task metrics, or an algebraic counterexample |
| Is same-frame adaptation causal? | C2 proof, implementation | Assimilation before the sample's correction, value-dependent stopping, or incorrect proposal probability |
| Does validity survive motion? | C3 proof, certificates | A geometry change exceeds the supplied bound; a numerical boundary error produces false acceptance |
| Are counts meaningful? | E11 report, query code | Warmup omitted, reference audit charged to the wrong side, or baseline recomputes already shared work |
| Does memory reduce actual error? | E10 CSV, protocol | Gains vanish against a strong matched-time/memory method |
| Is neural inference necessary? | E10 constant_certificate comparison |
Current comparison couples the neural predictor and proposal; no isolated proof of neural necessity |
| Is this beyond known methods? | Primary references, manuscript | Equivalent visibility-cache/certificate/control-variate interface already established |
Evidence boundaries
E10 has twelve new motion scenes, three fixed sampling seeds and 18,144 frame-method records. Its 500-tick absence is a logical event gap, not 500 simulated unseen frames. Geometry updates are authoritative. Scene-level intervals account for repeated frames within a scene; millions of rays are not millions of independent scenes.
E11 has eight further scenes and five prescribed times, with three known appearance readouts at each scene-time. It fully charges the initial anchor visibility queries. The stronger baseline already shares visibility across readouts. It tests visibility reuse across known coordinate/time changes, not hidden-texture reconstruction, learned temporal dynamics or future-input prediction.
The main CPU timing compares small batches and excludes common feature/prior preparation and independent reference audits. Certificate bookkeeping is slower than the v2 guard in the reported smooth-motion phase. Query savings are not a measured frame-time or GPU gain.
Proof and implementation gap
The sphere/segment certificate argument is in real arithmetic. The code uses float64 and a tolerance. Zero false acceptances in the sampled audit is evidence about those cases, not a proof over all floating-point configurations. Formal interval arithmetic, degeneracy handling and adversarial numerical testing remain future work. Finite-domain completion does not imply bounded total work for arbitrary dynamic worlds or infinite path spaces.
Decisive next experiments
- Implement the same contract in a GPU renderer and compare quality at matched end-to-end frame time and VRAM. Include certificate checking, memory traffic, updates and fallbacks.
- Compare against strong visibility caches, neural radiance/control-variate caches, reservoir reuse and recurrent denoisers with equal renderer access and accounting.
- Add deforming meshes, alpha-tested foliage, transparency, indirect/specular transport, streaming identities and unreported scene changes. Measure false-certificate rates and recovery.
- Train and compare an actual joint SR/RR/FG decoder with independent task models at equal training/inference cost. Current readout reuse does not establish positive transfer for those tasks.
- Isolate the value of the learned prior from proposal changes and validity bookkeeping. Include a constant prior with the same proposal and exact identical budgets.
Negative or null results should be retained. Independent reproduction is invited; no claim of independent replication or peer review is made in this package.
How to report an issue
Record the immutable Hub commit, operating system, Python/NumPy versions, protocol and seed, command, expected versus observed value, and a minimal reproducer. Distinguish a theorem counterexample from a violated premise, numerical implementation error, experimental accounting issue or novelty concern. Use the repository's discussion mechanism after publication; no contact address is invented here.