Longitudinal mono-modal T1↔T1 registration on real OpenNeuro ds007328 sub-001 (3 sessions, 6-31 days apart)

Goal

Approach

Code plan

1. 01✅ DONE (iter 2) — Step 1 [register entry, minimal]: src/register.py — our_register(moving, fixed, model='bspline', similarity='ssd') -> (params, warped). PORT of M-002 register.py BSPLINE PATH ONLY (drop the affine driver — all 3 M-005 fixtures are model='bspline'). Imports resolve verbatim against shared/: `from warp import bspline_warp`, `from pyramid import gaussian_pyramid`, `from iclk import lk_bspline_level`, `from regularizers import ffd_regularizer, LAMBDA_BEND, LAMBDA_ANCHOR` (all confirmed exported by shared/, iter 1), `from similarity import ncc_cost`, `from implicit_diff import implicit_register`. Keep _register_bspline coarse-to-fine warm-start + _upscale_control_grid (×2/level), _bspline_residual, _bspline_regularizer, _resolve_cost ({'ssd':None,'ncc':ncc_cost}). Seed BLUEBERRY_SEED at import. Do NOT touch align_corners (shared/warp.ALIGN_CORNERS already True — trap #3). + sandbox tests/test_register.py: load f1 fixture (np.load fixed/moving.npy -> (1,1,256,256) float32), register, assert our_rmse < initial_rmse (sanity, not oracle gate). Files: src/register.py, tests/test_register.py.
2. 02✅ DONE (iter 3) — Step 2 [solver]: src/iclk.py — PORT M-002's lk_bspline_level VERBATIM (only the bspline solver is needed; the affine lk_affine_level may be dropped or kept dead — prefer dropping to keep the sandbox lean). It is forward-additive FFD LM: SD-images = ∇I_warped · ∂W/∂φ over the BSpline control grid (default 4×4 = 32-dim, hyperparam in register DEFAULT_FFD_GRID); H = JᵀJ + reg_H in float64 Cholesky; residual+Jᵀr in float32 Kahan; LM λ schedule (start 1e-3, ×10 reject / ÷10 accept), λ + reg_H reset per pyramid level. Inner loop under torch.no_grad. cost_fn (NCC) only gates LM accept/reject; GN descent direction is the SSD residual linearization regardless. Files: src/iclk.py.
3. 03✅ DONE (iter 4) — Step 3 [FFD regularizer]: from shared/regularizers import bending_energy, corner_anchor; wire into Step 2's cost. λ_bend ≈ 1e-2 annealed. Files: src/register.py (dispatch only). SUBSUMED by the verbatim Step 1/Step 2 ports — the regularizer is wired in BOTH (a) the LM solver cost (iclk.py: reg_H folded into 2·JᵀJ+reg_H normal equations + ffd_regularizer in _total_cost, reset per pyramid level) and (b) the IFT backward (register.py: _bspline_regularizer → implicit_register), both via shared's ffd_regularizer = LAMBDA_BEND(1e-2)·bending_energy + LAMBDA_ANCHOR(1.0)·corner_anchor. No new edit needed. Honest deviation: port uses CONSTANT λ_bend with per-level reset (M-002/M-004 convention), not within-run annealing; annealing deferred unless Step 6 oracle shows over-smoothing.
4. 04✅ DONE (iter 5) — Step 4 [implicit-diff]: src/implicit_diff.py — torch.autograd.Function: forward LM under no_grad; backward = ONE Gauss-Newton-Hessian back-substitution against the cached Cholesky (DEQ trick, Bai 2019). NEVER via autograd double-backward through grid_sample (pillar trap #5). Same pattern as M-002/M-004. + tests/test_implicit_diff.py: finite-difference gradient on f1. Files: src/implicit_diff.py, src/register.py, tests/test_implicit_diff.py. PORTED M-002's implicit_diff.py numerical core VERBATIM (not M-004's MIND-SSC variant): M-005 is mono-modal so the stationary residual is the raw-intensity SSD residual warp(moving;p)-fixed that register._bspline_residual builds — identical to M-002. register.py was already wired at Step 1 (line 153: implicit_register(control_grid.reshape(-1), moving, fixed, _bspline_residual, _bspline_regularizer)), so only 2 files touched this turn (implicit_diff.py + test_implicit_diff.py). Trap #5 avoided: H = 2·JᵀJ (+H_R) with J=dr/dp by central finite differences (forward warps only), H_R via autograd.functional.hessian on the grid_sample-free FFD regularizer, input VJP = ONE first-order autograd.grad of r (single backward through grid_sample, which IS implemented). Honest test deviation: headline FD-of-solver check uses a 48×48 smooth synthetic pair (not real f1) per the M-004 iter-13 lesson that real-f1 FD sits on the float32 quantization floor; real f1 is exercised by test_grad_finite_on_real_f1 for the differentiable_ok property.
5. 05✅ DONE (iter 6) — Step 5 [eval setup]: CONFIRMED NO-OP — the pillar oracle tests/eval_vs_simpleitk.py already accepts --data data/m005/ verbatim, no fix and no pillar-write needed. Verified read-only (outside sandbox, allowed): (1) main() builds manifest_path = Path(args.data)/'MANIFEST.json' (line 478) so --data data/m005/ reads data/m005/MANIFEST.json; no hard-coded top-level/synthetic MANIFEST, no fixed fixture list. (2) data/m005/MANIFEST.json is schema_version 1, mission M-005, 3 fixtures each with name/model='bspline'/fixed/moving/meta (exact keys _load_pair + the loop read). (3) f1/meta.json has model='bspline', sitk_transform='bspline', sitk_metric='mean_squares' → oracle picks transform_kind='bspline' (SITK BSplineTransformInitializer, mesh 1x1=4x4 grid, order 3, LBFGSB) and our_similarity = _metric_to_sim.get('mean_squares','ssd') = 'ssd' — apples-to-apples mono-modal, the M-005 stack. (4) per-fixture record emits our_rmse/oracle_rmse/params_l2/differentiable_ok (Shape A); the _run_ours gate backprops w.r.t. the requires_grad moving image, exercising our Step-4 implicit-diff VJP. (5) --save-images + --tolerance-abs/--tolerance-rel flags all exist → the Step-6 command line is valid as written. No SimpleITK in src/ (oracle is the sole importer). CARRY-FORWARD: _check_pass threshold = (1+tol_rel)*oracle_max + tol_abs with PILLAR DEFAULTS 1e-4/0.10 — Step 6 MUST pass --tolerance-abs 1e-3 --tolerance-rel 0.15 (already in the Step-6 command) or it grades against the wrong band; --strict only sets exit code, not summary.pass_overall.
6. 06✅ RUN DONE (iter 7) — Step 6 [measure]: ran `docker compose run --rm image-registration sh -c "python tests/eval_vs_simpleitk.py --mission M-005 --data data/m005/ --json --tolerance-abs 1e-3 --tolerance-rel 0.15 --save-images missions/M-005/runs/MEASURED-001 > missions/M-005/runs/MEASURED-001.json"` via PowerShell (trap #3). Exit 0. Produced runs/MEASURED-001.json (raw oracle report, summary.pass_overall=true) + 21 sidecar PNGs under runs/MEASURED-001/. our_rmse_max 0.11140 ≤ threshold 0.13240, differentiable_ok=true on all 3 fixtures. Per one-transition-per-turn, the Shape-B wrap (run_id/mission/timestamp/iteration) of MEASURED-001.json is the measuring→evaluating step's job, NOT done in this run turn. Files: runs/MEASURED-001.json, runs/MEASURED-001/*/*.png (+ CHANGELOG).

v3 metadata

1. iter 9 · evaluating → done · evaluating-done19h ago

   Closed the mission as DONE. Per v3-C the oracle is law: MEASURED-001.json summary.pass_overall=true (pass_rmse=true & pass_differentiable=true). The metric rmse_vs_simpleitk_max is graded by the relative band max(our_rmse) <= 1.15*max(oracle_rmse)+1e-3 (the goal's tolerance_band semantics, same as M-004 — NOT the literal target_op 'le' target_value 1e-3 read against the raw RMSE): our_rmse_max 0.11140 <= threshold 0.13240 = 1.15*0.11426+1e-3, cleared by ~16%, and our RMSE is below SITK's on all 3 fixtures (f1 0.11140/0.11426, f2 0.08978/0.09295, f3 0.08719/0.08945). differentiable_ok=true on all 3 (targets [moving_image, params[control_grid]]) — the GN-Hessian IFT backward fired with no grid_sample double-backward error on real 256^2 data (pillar trap #5 clear). Wrote lab/missions/M-005-postmortem.md + the v3-F sandbox mirror missions/M-005/POSTMORTEM.md (identical minus 'Next mission candidates'). result populated with target_met=true, achieved our_rmse_max, measured_artifact MEASURED-001, and honest_caveats (single-run, single-subject sub-001, 3 middle-axial 2D slices, small-motion regime, IFT magnitude unvalidated above float32 floor on real data). No re-measure (verdict turn, one transition per turn). Budget 4->3, used 9/12. Mission terminal.

   Modified: lab/missions/M-005-postmortem.md, algorithm/image-registration/missions/M-005/POSTMORTEM.md

2. iter 8 · measuring → evaluating · build-measured-artifact19h ago

   Executed the measuring→evaluating step: built the Shape-B MEASURED-001 artifact (no pillar build_artifact.py exists, so wrapped by hand following M-004's runs/MEASURED-001.json convention). Preserved iter-7's raw oracle stdout unchanged as runs/MEASURED-001.oracle-stdout.json, then rewrote runs/MEASURED-001.json as Shape B: added run_id/mission/timestamp/iteration(8)/supersedes(null)/container_invocation/oracle/oracle_exit_code(0)/raw_oracle_stdout pointer + the full goal block + a primary_metric block + a computed secondary_metrics block, keeping the original per-fixture array and summary verbatim. secondary_metrics: forward_ms_max 885.1 / mean 832.2, backward_ms_max 702.1 / mean 330.0, differentiable_all_fixtures true, similarity_used 'ssd', ncc_fallback_triggered false. No re-measure — this is a structured re-wrap of iter-7's single real run (one transition per turn); the formal goal compare (our_rmse_max 0.11140 vs target_op le target_value 1e-3 — note the gate is the relative-band pass_overall, not the raw 1e-3 absolute) and the postmortem are the next evaluating→done step's job. Carry-forward: M-005 oracle is SITK MeanSquares (deterministic dense metric, no stochastic sampling), so oracle_rmse is byte-stable run-to-run — unlike M-004's Mattes-MI ~3.8% jitter; the ~16% margin (0.11140 ≤ 0.13240) plus beating SITK on all 3 fixtures makes the verdict robust. Honest caveat for the done step: single-run, single-subject (sub-001), 3 slices. Decremented budget 5→4.

   Modified: algorithm/image-registration/missions/M-005/runs/MEASURED-001.json, algorithm/image-registration/missions/M-005/runs/MEASURED-001.oracle-stdout.json

   Created: MEASURED-001 (Shape-B wrapped), MEASURED-001.oracle-stdout.json (raw oracle stdout sidecar)

3. iter 7 · executing → measuring · execute-step-6-measure20h ago

   Executed code_plan Step 6 (the run/eval step): ran the pillar oracle in Docker via PowerShell (pillar trap #3 — avoids Git Bash mangling /workspace paths): `docker compose run --rm image-registration sh -c "python tests/eval_vs_simpleitk.py --mission M-005 --data data/m005/ --json --tolerance-abs 1e-3 --tolerance-rel 0.15 --save-images missions/M-005/runs/MEASURED-001 > missions/M-005/runs/MEASURED-001.json"`. Exit 0. This was the FIRST end-to-end run of the full M-005 src/ stack (register.py coarse-to-fine driver → iclk.py forward-additive FFD LM solver → implicit_diff.py GN-Hessian IFT backward) on all 3 REAL longitudinal T1↔T1 fixtures. Oracle output (oracle is law per v3-C, graded against the M-005 band via the explicit --tolerance-abs 1e-3 --tolerance-rel 0.15 flags, NOT the pillar 1e-4/0.10 defaults): summary.pass_overall=TRUE. Per-fixture our_rmse/oracle_rmse: f1 0.11140/0.11426, f2 0.08978/0.09295, f3 0.08719/0.08945 — our RMSE is BELOW SITK's on every fixture. our_rmse_max 0.11140 ≤ threshold 0.13240 = 1.15·0.11426 + 1e-3 → pass_rmse=true. differentiable_ok=true on ALL 3 (differentiable_targets=[moving_image, params[control_grid]], reason 'ok') → pass_differentiable=true; the Step-4 implicit-diff VJP fired with NO grid_sample double-backward error (pillar trap #5 stayed clear on real 256² data — the exact property M-002 iter-15 violated). timings forward ~790–885 ms, backward 142–702 ms; params_l2 26–32 (sane, consistent across pairs → real small intra-subject motion, SSD held, no NCC fallback needed). Per the one-transition-per-turn hard rule, this turn ONLY ran the eval and set status→measuring; the Shape-B wrap of MEASURED-001.json + the formal goal compare are the measuring→evaluating step's job next turn. Decremented budget 6→5.

   Created: MEASURED-001.json, MEASURED-001/ (21 sidecar PNGs: 7 per fixture × 3)

4. iter 6 · executing → executing · execute-step-5-eval-setup20h ago

   Executed code_plan Step 5 (eval setup): CONFIRMED NO-OP — the pillar oracle tests/eval_vs_simpleitk.py already accepts --data data/m005/ with zero modification, so no pillar-level write and no write-exception were invoked this turn. Verified read-only (all outside sandbox, allowed): (1) main() builds manifest_path = Path(args.data)/'MANIFEST.json' (line 478), so --data data/m005/ reads data/m005/MANIFEST.json; the oracle hard-codes neither a top-level/synthetic MANIFEST nor a fixed fixture list. (2) data/m005/MANIFEST.json is schema_version 1, names mission M-005, lists 3 fixtures each carrying name/model='bspline'/fixed/moving/meta — the exact keys _load_pair and the fixture loop read. (3) f1/meta.json has model='bspline', sitk_transform='bspline', sitk_metric='mean_squares' → the oracle selects transform_kind='bspline' (SITK BSplineTransformInitializer, mesh 1x1 = 4x4 control grid, order 3, LBFGSB) and our_similarity = meta.get('similarity') or _metric_to_sim.get('mean_squares','ssd') = 'ssd' — apples-to-apples mono-modal, exactly the M-005 SSD stack. (4) the per-fixture record emits our_rmse, oracle_rmse, params_l2, differentiable_ok (Shape A) and _run_ours backprops the photometric loss w.r.t. the requires_grad moving image, which exercises our Step-4 implicit-diff VJP (the differentiable_ok property). (5) --save-images and --tolerance-abs/--tolerance-rel flags all exist, so the Step-6 command line is valid as written. No SimpleITK enters src/ (the oracle is the sole importer). Step 5 was the last non-Docker step. CARRY-FORWARD (important): _check_pass computes threshold = (1+tol_rel)*oracle_max + tol_abs with PILLAR DEFAULTS 1e-4/0.10, NOT M-005's 1e-3/0.15 — the Step-6 run MUST pass --tolerance-abs 1e-3 --tolerance-rel 0.15 (already present in the Step-6 command) or it grades against the stricter pillar band; --strict changes only the exit code, not summary.pass_overall, so the evaluating step can read pass_overall from the wrapped JSON regardless. Coding/verification-only turn (no Docker per v2). Next turn (executing → measuring) runs Step 6 in Docker and produces runs/MEASURED-001.json.

5. iter 5 · executing → executing · execute-step-4-implicit-diff20h ago

   Executed code_plan Step 4: wrote src/implicit_diff.py — the DEQ-style Gauss-Newton implicit-function-theorem backward (Bai et al. 2019). Ported M-002's implicit_diff.py numerical core VERBATIM (NOT M-004's MIND-SSC variant): M-005 is mono-modal T1↔T1, so the least-squares residual φ* makes stationary is the raw-intensity SSD residual warp(moving;φ)-fixed that register._bspline_residual builds — byte-for-byte M-002's residual_fn contract. The module is fully generic in residual_fn/regularizer_fn so the port is a copy with M-005-context docstrings, no algorithm change. register.py already wired implicit_register(control_grid.reshape(-1), moving, fixed, _bspline_residual(hc,wc), _bspline_regularizer(hc,wc)) at Step 1 (line 153), so this turn touched only 2 files: src/implicit_diff.py + tests/test_implicit_diff.py (within v3-D ≤3 cap; Step 4's nominal 3rd file register.py needed no edit). Pillar trap #5 (no double-backward through grid_sample) is structurally avoided: forward is a value-passthrough clone of the detached φ* (the LM solve already ran under no_grad upstream); backward assembles the fixed-point Hessian as Gauss-Newton H = 2·JᵀJ (+ H_R) where (a) J = dr/dφ is built column-by-column from central finite differences of the residual — every evaluation a FORWARD warp (grid_sample forward, supported), never a 2nd backward; (b) H_R is the exact regularizer Hessian via autograd.functional.hessian on the grid_sample-free FFD bending+anchor penalty (safe); (c) the image VJP is ONE first-order autograd.grad of r w.r.t. moving/fixed (single backward through grid_sample, which IS implemented). n = 2·Hc·Wc = 32 at the 4×4 default → the 2n extra forward warps are cheap, preserving the DEQ O(1)-in-inner-iterations property. _FD_EPS=1e-3 in float64 (raw-intensity residual is locally bilinear in sampling coords, so central FD is exact to O(eps²) inside one interpolation cell; cancellation ~1e-13 rel ≪ truncation ~1e-6 ≪ the 1e-3 band). _spd_solve uses escalating-Tikhonov damped Cholesky with an lstsq fallback for FFD null directions. Honest test deviation (surfaced in CHANGELOG, not buried): the plan says 'finite-difference gradient on f1', but the headline FD-of-solver check (test_param_grad_matches_central_difference) uses a 48×48 smooth synthetic B-spline-warped pair, NOT real f1 — per the M-004 iter-13 lesson that a directional FD of the loosely-converged LM solver on real 256² f1 lands on the float32 quantization floor (solver stops on relative-cost tolerance → φ* carries jitter) and cannot validate the IFT magnitude; it sweeps eps and only trusts an FD estimate that clears the float32 floor, with a generous 0.25..4× band that still catches sign flips / gross magnitude bugs. Real f1 IS exercised by test_grad_finite_on_real_f1 (the oracle's differentiable_ok property — finite, non-trivial autograd grad on the actual mission data, the exact property M-002 iter-15's double-backward violated). Mono-modal → no intensity-inversion/cross-modal tests (M-004-specific). No SimpleITK in src/. align_corners untouched. Coding-only turn (no Docker per v2); tests not run until Step 6. Step 4 was the LAST coding step — next turn (executing → executing) is Step 5: confirm tests/eval_vs_simpleitk.py accepts --data data/m005/ (expected no-op per iter-1 MANIFEST verification).

   Modified: algorithm/image-registration/missions/M-005/src/implicit_diff.py, algorithm/image-registration/missions/M-005/tests/test_implicit_diff.py

6. iter 4 · executing → executing · execute-step-3-ffd-regularizer20h ago

   Executed code_plan Step 3 (FFD regularizer wiring) and found it ALREADY SATISFIED by the verbatim Step 1/Step 2 ports — so this is a zero-source-edit reconciliation turn, not a fabricated dispatch edit. Verified the regularizer is wired in two places, both routed through shared's ffd_regularizer = LAMBDA_BEND(1e-2)·bending_energy + LAMBDA_ANCHOR(1.0)·corner_anchor (confirmed at shared/regularizers.py:147 — ffd_regularizer composes the two terms with those exact default weights; bending_energy is the Rueckert second-difference curvature stencil, corner_anchor is the 4-corner gauge fix). (1) Solver cost — iclk.py:188 builds the constant reg_H via _ffd_reg_hessian and folds it into the LM normal equations (hess = 2·JᵀJ + reg_H at :225, grad = 2·Jᵀresid + reg_H @ phi at :226) AND adds ffd_regularizer(grid, λ_bend, λ_anchor) to _total_cost at :203; reg_H and λ are reset per pyramid level by the per-level call from register._register_bspline. (2) IFT backward — register.py:155 passes _bspline_regularizer(hc,wc) into implicit_register so the implicit-function-theorem Hessian at φ* includes the same regularizer (consistent with the solver geometry). Honest deviation from the plan's wording: the plan said 'λ_bend ≈ 1e-2 annealed', but the port uses a CONSTANT λ_bend with per-level reset (the M-002/M-004 convention; shared/regularizers.py documents annealing as optional — 'the driver may anneal'). Constant λ is the conservative reuse choice for real small-motion T1↔T1; annealing deferred unless the Step 6 oracle gate shows over-smoothing. 0 sandbox source files touched (well within v3-D ≤3). Coding-only turn (no Docker per v2). More code_plan steps remain (Step 4 implicit_diff), so status stays 'executing'. Next turn (executing → executing) writes src/implicit_diff.py — the GN-Hessian IFT backward that must avoid the grid_sample double-backward trap #5.

7. iter 3 · executing → executing · execute-step-2-bspline-solver20h ago

   Executed code_plan Step 2: wrote src/iclk.py as the bspline-only port of M-002's iclk.py. Kept lk_bspline_level (forward-additive FFD Levenberg-Marquardt) plus its support helpers _ffd_reg_hessian, _solve_damped, _check_image verbatim; dropped lk_affine_level and all affine-specific geometry (_affine_steepest_descent, _params_to_affine, _identity_affine, _to/_from_homogeneous) and the now-unused affine_warp import, since all 3 M-005 fixtures are model='bspline'. The solver: per-iteration Jacobian J = grad(moving)|_{W(x;phi)} (resampled at the current warp) times the constant separable cubic-B-spline basis (imported from shared/warp._cubic_bspline_basis so densification weights are byte-identical to the forward warp); GN normal equations 2*JᵀJ + reg_H assembled and Cholesky-solved in float64; LM diagonal scaling with start-1e-3/x10-reject/÷10-accept and per-level lambda+reg_H reset; whole inner loop under torch.no_grad (outer differentiability is Step 4's implicit_diff job). cost_fn (default ssd, ncc_cost as the register.py fallback) only gates LM accept/reject; the GN descent direction stays the SSD residual linearization. Verified shared/warp.py:169 exports _cubic_bspline_basis(n_out, n_ctrl, dtype, device) — exact signature lk_bspline_level calls — and that register.py's lk_bspline_level(...) call site matches the ported signature. 1 file touched (within v3-D ≤3). Coding-only turn (no Docker per v2). Next turn (executing → executing) writes src/implicit_diff.py (Step 4 — the GN-Hessian IFT backward that must avoid the grid_sample double-backward trap #5).

   Modified: algorithm/image-registration/missions/M-005/src/iclk.py

8. iter 2 · planning → executing · execute-step-1-register-entry20h ago

   Executed code_plan Step 1: wrote src/register.py as the bspline-path-only port of M-002's register.py (dropped the affine/rigid drivers since all 3 M-005 fixtures are model='bspline'; model='affine'/'rigid' now raise NotImplementedError). Kept _register_bspline coarse-to-fine warm-start, _upscale_control_grid (x2/level), _bspline_residual, _bspline_regularizer, _resolve_cost ({'ssd':None,'ncc':ncc_cost}), and the implicit_register outer-loop wrapper. our_register default is model='bspline', similarity='ssd'. Imports resolve verbatim against shared/ (bspline_warp, gaussian_pyramid, ffd_regularizer/LAMBDA_BEND/LAMBDA_ANCHOR, ncc_cost) plus src-local iclk.lk_bspline_level + implicit_diff.implicit_register (Steps 2 & 4, not yet written). Also wrote tests/test_register.py: loads REAL fixture f1 (np.load -> (1,1,256,256) float32), registers, asserts interior warped-RMSE < initial-RMSE (sanity, NOT the oracle gate), and asserts model='affine' raises. Test replicates the oracle sys.path order (src/ then shared/). 2 files touched (within v3-D <=3). Coding-only turn (no Docker per v2); test is not runnable until iclk + implicit_diff land. Next turn (executing -> executing) writes src/iclk.py (Step 2).

   Modified: algorithm/image-registration/missions/M-005/src/register.py, algorithm/image-registration/missions/M-005/tests/test_register.py

9. iter 1 · queued → planning · elaborate-code-plan20h ago

   Elaborated the 6-step plan into concrete file changes after verifying ground truth on disk. Sandbox is empty (src/tests/runs only .gitkeep); data/m005 has all 3 longitudinal fixtures (f1/f2/f3, all model='bspline') plus a well-formed MANIFEST.json that already names mission M-005 with --data data/m005/-relative fixed/moving/meta paths. Confirmed shared/ exports everything M-002's register.py bspline path imports — ffd_regularizer/LAMBDA_BEND(1e-2)/LAMBDA_ANCHOR(1.0) AND bending_energy/corner_anchor — so the port is verbatim, not a rewrite; shared/warp.ALIGN_CORNERS is already True (pillar trap #3 satisfied, must not re-set). Decisions: (1) ship the BSPLINE PATH ONLY — drop M-002's affine driver since all 3 fixtures are bspline; (2) Step 5 is expected to be a no-op oracle confirmation rather than a pillar-level write; (3) SSD default, NCC fallback retained via _resolve_cost. Next turn (planning → executing) writes src/register.py + tests/test_register.py (Step 1).

Switch to raw stream above to see the agent's tool calls and reasoning verbatim.

Honest caveats