Renderer Stalled Parity Triage

Renderer Stalled Parity Triage

Purpose

Stop unbounded beauty-image tuning when evidence says the problem is structural, stochastic, architectural, or under-specified. Turn a stalled comparison into testable hypotheses with clear stop conditions. If narrow hypotheses keep failing, switch to a no-sacred-cows architecture pivot instead of making the next edit smaller.

Trigger

Use this workflow when any of these are true:

• Beauty output has not materially improved after two or more targeted experiments.
• Several target-shaped vertical slices have been rejected and the same residual structure remains.
• Local patches are making the code harder to reason about while preserving the same visual failure.
• RMS/MAE improves but signed residuals, crops, or AOVs still show the same visual failure.
• Residuals are spatially structured: missing bands, contact shadows, highlights, instances, silhouettes, texture regions, or lighting gradients.
• Noise, fireflies, clamping, or denoising changes the apparent result more than the semantic change under test.
• The reference renderer's camera, post settings, color management, light policy, or feature support is not fully known.
• Frame time or driver instability prevents rapid iteration on the current path.

Workflow

1. Freeze the current baseline.

   • Record command, scene, camera, reference, resolution, spp, max depth, seed, exposure, tone map, denoise/filter, texture/resource caps, backend, frame time, and artifact paths.
   • Save raw/linear output when possible, plus tonemapped beauty, signed residual, and the most relevant AOVs.
   • List recent rejected experiments and the evidence that rejected each one.

2. Read the residual as a map, not a score.

   • Divide the image into named regions or feature classes: foreground, background, high-error clusters, shadow bands, highlights, thin geometry, transparent regions, interiors/exteriors, sky/environment, and material groups.
   • Mark whether each region is too bright, too dark, too noisy, too smooth, spatially shifted, missing structure, or wrong color.
   • Use crops and local statistics so small but important features are not averaged away.

3. Separate semantic mismatch from convergence and reconstruction.

   • Compare raw/no-denoise and denoised outputs.
   • Compare direct, visibility, environment, indirect, normal, depth, albedo/basecolor, material ID, and opacity/transmission AOVs when available.
   • If a term AOV contains the missing structure but beauty loses it, capture the exact buffers entering reconstruction: unclamped and clamped direct, residual before denoise, residual after firefly clamp, direct after filtering, final composite, variance, and any history/confidence data.
   • If a reuse or reservoir candidate AOV looks plausible but beauty regresses, classify the pass as scaffolding until candidate validity, target PDFs, final visibility, merge weights, and the residual/direct composite contract are proven together.
   • For path-traced lighting failures, classify direct illumination, GI, and denoising separately. Direct-light instability points toward sampling/reuse/final visibility; indirect fireflies point toward GI reuse/path guiding/variance controls; denoiser blur points toward missing guides, hit distances, or signal splits.
   • Run fixed-seed and changed-seed captures to distinguish deterministic bias from variance.
   • Prefer equal-time or equal-budget comparisons for path-traced changes; equal-spp comparisons can mislead when one method changes variance or frame time.

4. Build a hypothesis ledger.

   • For each proposed change, write the predicted region/AOV effect, smallest test, pass signal, fail signal, and expected frame-time impact.
   • Do not repeat a rejected experiment unless new evidence invalidates its rejection.
   • Change one meaningful axis at a time unless the hypothesis requires a coupled change.
   • When an experiment improves an internal AOV but not beauty, keep the artifact and rejection reason in the ledger. Do not continue tuning local weights unless the next hypothesis predicts a specific regional change in the signed residual.
   • If the ledger contains multiple rejected variants of the same subsystem, mark that subsystem as suspect. The next hypothesis should be allowed to replace it, not just adjust it.
   • If replacing the suspect subsystem is the first test that can falsify the new hypothesis, do that replacement instead of inserting another transitional knob.

5. Classify the next pivot.

   • If load/material/light/count AOVs are wrong, return to scene extraction or material/light fixtures.
   • If direct/visibility AOVs imply plausible light but the residual is spatially blocked or opened in the wrong places, check geometry visibility semantics before light tuning: inherited visibility, purpose, shadow flags, opacity, and authored versus schema-fallback sidedness such as Mesh doubleSided.
   • If visibility AOVs contain the missing structure but beauty does not, inspect sampling allocation, MIS, clamping, denoising, and accumulation.
   • If direct terms are plausible but indirect is noisy or firefly-prone, prioritize transport variance reduction, path guiding, sample allocation, or reconstruction.
   • If direct and indirect term AOVs both show useful signal but beauty is flat, stop local tuning and plan a target-shaped reconstruction slice: stable direct-light estimates, stable GI estimates, and a denoiser input contract with normal/depth/albedo/roughness/motion/hit-distance/history guides.
   • If raw output is plausible but tonemapped output is not, prioritize reference post settings, exposure, color management, and output transforms.
   • If denoising erases structure or spreads outliers, prioritize guided spatial/temporal denoising with normal/albedo/depth/history inputs.
   • If iteration is too slow, prioritize resident rendering, reduced probes, hardware RT/ray query, pass gating, or smaller representative fixtures before more beauty sweeps.
   • If reference metadata is missing, mark parity blocked or degraded until it is recovered or an explicit approximation contract is written.
   • If local fixes keep preserving the same wrong image, pursue a radical but evidence-backed change: replace the sampler, reconstruction contract, denoiser, scene extraction policy, or fixed checkpoint shape. Keep the old path only as a comparison baseline or rollback point.

6. Exit with a decision.

   • Name the best current hypothesis, the rejected hypotheses, and the next smallest command.
   • State whether the next move is correctness, sampling/convergence, denoising/reconstruction, reference metadata, performance architecture, or a broader subsystem replacement.
   • When the decision is a broader replacement, name the old path to bypass or retire and the minimum artifact that will prove the new architecture is worth keeping.
   • Leave a matrix row or handoff note so the next agent does not restart the same scalar sweep.

Anti-Patterns

• Continuing exposure, tone map, light-scale, material-mode, or denoiser sweeps after the residual pattern has stopped changing.
• Claiming progress from a scalar metric while the same regional failure remains visible.
• Comparing only equal spp when one method is much slower or has different variance.
• Treating denoising or clamping as a substitute for plausible transport AOVs.
• Reusing a direct AOV as proof of beauty correctness when the beauty path clamps, subtracts, filters, or composites a different buffer.
• Ignoring unknown reference renderer settings while tuning local constants to match one image.
• Keeping rejected experiments only in prose without a clear reason not to repeat them.
• Treating the existing renderer architecture, denoiser, sampler, or comparison setup as a constraint after evidence says it is the problem.
• Responding to repeated failed slices by shrinking scope again instead of changing the hypothesis class.