V6a.1 Fixed Ray-Evidence Transport Implementation Plan

For agentic workers: REQUIRED SUB-SKILL: Use superpowers:test-driven-development for every behavior change and superpowers:verification-before-completion before any success claim.

Goal: Add a versioned, target-independent fixed ray-evidence transport architecture and run its frozen one-window capacity gate.

Architecture: Reuse V6a's fixed local candidate scores, normalize by actual observer count, require two independent observers, convert valid-only softmax excess into trilinearly transported native-grid evidence, and keep the existing learned residual plus fixed renderer unchanged.

Tech Stack: Python 3.11, PyTorch 2.7+, pytest, Ruff, YAML configs, JSON/JSONL artifacts.

Global Constraints

• Never alter V5 evidence_residual or V6a dual_evidence artifact semantics.
• No target label may enter evidence or state construction.
• No learned transport, learned renderer, target branch, image warp, Gaussian primitive, or new trainable parameter.
• Use seed 17 and the existing 48x32x48, 4-context/2-target, 64-sample one-window protocol.
• This directory has no Git repository; use the versioned backups/ snapshot and fresh outputs instead of commit steps.

Task 1: Mass-conserving trilinear splat reducer

Files: - Modify: src/mcss/model/evidence_encoder.py - Test: tests/test_evidence_encoder.py

Interfaces: - Produces _trilinear_splat_candidates(points, values, valid, bounds, resolution) -> Tensor. - Input points are [B,C,K,3]; values/valid are [B,C,K]; output is [B,N].

• [ ] Write tests where an exact voxel center receives all mass, an off-center point splits mass across eight cells, and an AABB-edge point preserves total mass after invalid-neighbor renormalization.
• [ ] Run uv run --extra dev pytest tests/test_evidence_encoder.py -q and confirm the new tests fail because the reducer is absent.
• [ ] Implement FP32 scatter_add_ with center-coordinate conversion, eight fixed neighbor offsets, in-grid masking, and per-candidate weight renormalization.
• [ ] Re-run the focused tests and require all to pass.

Task 2: Versioned transport evidence type and configuration route

Files: - Modify: src/mcss/types.py - Modify: src/mcss/model/system.py - Modify: src/mcss/config.py - Test: tests/test_types.py - Test: tests/test_config.py

Interfaces: - Produces TransportEvidence with transported localization, transported appearance, transported peakness, coverage, V5 appearance fields, residuals, gates, and exact density/color properties. - Adds SceneState.transport_evidence and architecture name dual_evidence_transport.

• [ ] Write failing tests for value ranges, c_loc=c_app_transport*peak_transport, reconstruction, .to(), mutually exclusive evidence payloads, YAML parsing, and unchanged V5/V6a defaults.
• [ ] Run the two focused test files and confirm expected failures.
• [ ] Add the dataclass, SceneState validation/transfer, architecture literal, config allowlist, and encoder routing without adding model parameters.
• [ ] Re-run focused tests and require all to pass.

Task 3: Fixed transport encoder

Files: - Modify: src/mcss/model/evidence_encoder.py - Modify: src/mcss/model/system.py - Test: tests/test_evidence_encoder.py

Interfaces: - Produces TransportEvidenceStateEncoder and _transport_surface_evidence(...). - Consumes V5 appearance confidence and completion residuals; returns transported native-grid R, A/R, M/A, and M/R fields.

• [ ] Write failing tests for flat-profile zero mass, off-center peak displacement, two-view zero support, view-permutation invariance, detached evidence, unchanged parameter keys, and exact density reconstruction.
• [ ] Run focused tests and confirm failures are due to missing transport behavior.
• [ ] Implement the chunked reference/candidate loop with actual-observer score mean, minimum two observers, valid-only excess, and three splat accumulators.
• [ ] Keep color and high-resolution appearance exactly on the V5 path.
• [ ] Re-run focused tests and require all to pass.

Task 4: Training diagnostics and evidence audit compatibility

Files: - Modify: src/mcss/engine.py - Modify: src/mcss/evidence_audit.py - Modify: src/mcss/evidence_telemetry.py - Test: tests/test_evidence_encoder.py - Test: tests/test_evidence_audit.py - Test: tests/test_evidence_telemetry.py

Interfaces: - Engine logs transported support/peakness/coverage, gates, and rewrite energy. - Surface audit selects transport_evidence.surface_localization_support for V6a.1.

• [ ] Write failing tests for regularization selection, diagnostics keys, audit field selection, and telemetry replay.
• [ ] Implement transport-specific branches without changing legacy/V5/V6a keys.
• [ ] Run all three focused test files and require all to pass.

Task 5: Configs, static verification, and real-data gate

Files: - Create: configs/synthetic_v6a1_transport_smoke.yaml - Create: configs/hypersim_er_v6a1_transport_overfit.yaml - Create runtime artifacts only below outputs/hypersim_er_v6a1_transport_*.

• [ ] Add isolated synthetic and Hypersim configs with seed 17, fixed renderer, unchanged capacity, surface_peak_temperature: 0.05, and a unique output directory.
• [ ] Run focused tests, full pytest, Ruff, and compileall.
• [ ] Run CPU synthetic train/evaluate and the existing CUDA smoke matrix extended with the new architecture; require finite outputs and zero renderer parameters.
• [ ] Train exactly 5,000 one-window steps, evaluate step 5000 in a fresh output directory, and write a gate report against V5 step 4500.
• [ ] If any capacity threshold fails, seal the failure and stop the branch. If all pass, run the frozen 32-window validation surface audit before considering V6b.