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1. Jammy2211 commented on Jul 30, 2026

   

   Jammy2211

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   Phase 1 shipped — smoke timeout fix: autolens_workspace_test#238 (open, pending-release).

   Root cause of the flake: the entry ran at 232s/224s against the 300s cap. cProfile of one LM outer iteration (156.6s local) showed ~46% in the KNN source-interpolator rebuild (dominated by numpy.asarray over the over-sampled 5835×16-point data grid) and ~42% in the 5835² dense PSF product of dpsi_mapping_matrix_from; the final log_evidence() pays a full extra Jacobian rebuild. Fix: over_sample_size 4→2 + n_iter 5→3 — total 810s→340s local (projected ~100–140s CI), recovery metrics improved (corr 0.78→0.82, peak dist 0.16"→0.06", both legs).

   Heart was YELLOW at ship (manifest drift: tenant firewall — 1 mismatch vs repos.yaml; plus 2 pre-existing STALE reasons); human acknowledged.

   Phase 2 underway — reference tree from for_qiuhan_PT_jax.tar restored (ships the 200×200 demo dataset + the author's own legacy-vs-JAX parity harness). First structural divergence already identified for testing: the original LM engine damps with H + mu*I (numpy branch AND its jax_ops) and cold-starts from zeros; the port uses Marquardt mu*diag(H) damping (dense_util.py:390, deliberate — scale-invariance for uv curvatures) and required the #630 warm start after cold-start collapsed. Experiment matrix: tar-legacy vs port vs port-with-identity-damping, all cold-start at the author's demo-2 hyper-params (Matern52 c=803465.55, s=0.4497), plus a one-shot parity anchor at c=2000/s=4.
2. Jammy2211 commented on Jul 30, 2026

   

   Jammy2211

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   Phase 2 first results — single-step method is in FULL parity; discrepancy localized to the iterative path.

   Side-by-side on the identical 200x200 demo dataset from the tar (both engines consuming the same masked_imaging object):

   • Mesh/operator parity: dpsi mesh points and the Hamiltonian (dpsi→dkappa) operator are exactly identical (max diff 0.0). Matern reg matrix agrees to ~3e-7 relative (float noise).
   • One-shot joint inversion (Cao single-step) at c=2000/s=4: log evidence 1.693447e4 both engines (diff 5.9e-7); corr(dkappa_tar, dkappa_port) = 1.000000; max|dpsi diff| = 3.5e-7. Both recover corr 0.7767, peak 0.205" — the certified demo numbers. No bug in the single-step method.

   Now running (detached, sequential): the iterative experiment matrix — tar-original vs port vs port-with-identity-damping-patch, all cold-start at the author's demo-2 hyper-params (Matern52 c=803465.55, s=0.4497), plus both engines at c=2000/s=4. Hypothesis under test: the port's Marquardt mu*diag(H) damping (vs the original's mu*I) changes the LM trajectory enough to lose the subhalo basin from a cold start at the author's params — which would fully explain "same regularization parameters, different recovered signal".
3. Jammy2211 commented on Jul 30, 2026

   

   Jammy2211

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   Discrepancy REPRODUCED and mechanism identified (pending final confirmation run).

   Cold start at the author's demo-2 params (Matern52 c=803465.55, s=0.4497), identical inputs and geometry (8977 data / 2168 dpsi pixels), n_iter=5:

   Iter	author engine (H + mu*I) cost	port (H + mu*diag(H)) cost
   0	5.0276e6	5.0276e6 (identical → cost functions agree exactly)
   1	1.2135e4	9.2339e4
   4	6.2694e3	1.0367e4

   Both accept every step with the same mu schedule (1.0 → 0.012). The difference is purely the damping matrix: at mu=1, identity damping barely perturbs high-curvature directions (near full Gauss-Newton first step → author converges in ~3 iterations), while Marquardt mu*diag(H) scales the damping to the curvature and roughly halves every step — after the 5-iteration budget the port is still far from the optimum and its recovered dkappa is noise: corr 0.024, peak 2.58" off (vs the author's healthy convergence; their reference Laplace evidence 17593.4 on the fixture geometry).

   So "same regularization parameters, different recovered mass-perturbation signal" is fully explained by the port's deliberate damping-form change (dense_util.py:390), not by a numerical bug — the port needs either identity damping (the published algorithm) or a much larger iteration budget. iter_port_iddamp (port with mu*I restored, all else identical) is running now; if it matches the author's trajectory, the fix is a damping option on the LM engines with the imaging default restored to identity (interferometer may keep Marquardt for its ~1e11 visibility-curvature scales — decision at fix time).

   Note: the author-engine run was OOM-killed after its 5th iteration on this box (it builds a dense 8982² noise-covariance matrix; the port uses a vector), so its cost trace above is complete but final dkappa metrics for it will come from a re-run with the diagonal covariance held sparse if needed.
4. Jammy2211 commented on Jul 30, 2026

   

   Jammy2211

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   ROOT CAUSE CONFIRMED — the two implementations are equivalent; the discrepancy was the damping form. Checkpoint (session pausing overnight).

   iter_port_iddamp (port with mu*I restored, cold start, author params): cost trajectory matches the author's engine to 4 significant figures at every iteration (5.0276e6 → 1.2135e4 → 6.4004e3 → 6.2716e3 → 6.2696e3 vs the author's 6.2694e3). Conclusions:

   1. No implementation bug. One-shot: machine-precision parity. Iterative: once damping matches, the engines produce the same trajectory and optimum.
   2. The user-visible discrepancy = the port's Marquardt mu*diag(H) damping under-converges within a small n_iter budget (corr 0.024 vs the converged 0.177 at the same params) — "same regularization parameters, different recovered signal", exactly as reported.
   3. At the author's stored demo-2 params, the converged optimum itself scores weakly (corr 0.177, peak 2.33" — matching Phase 5's earlier 0.18/1.82" measurement). Both codes agree there; the params are genuinely sub-optimal per evidence (c=2000/s=4 had higher Laplace evidence in Phase 5). The evidence-sampled acceptance test (Phase 3, the author's own proposal) is the principled remedy.
   4. New finding — end-of-budget rejection storm (both engines): at convergence no cost-decreasing step exists, so the inner LM loop rejects ~23 consecutive trials (each a full ~2-min Jacobian rebuild) until mu > 1e15 — 47 wasted minutes. The step-tolerance check only fires on accepted steps. The author's engine likely OOM-died in this same storm (rejected trials allocate fresh dense matrices). Fix candidates for the library PR: damping mode option (identity default for imaging, Marquardt retained for uv), stall detection on consecutive rejections, tol check on the rejected-step norm.

   Still running detached overnight: both engines at the calibrated c=2000/s=4 (expect corr ~0.73 and agreement → full vindication). Artifacts auto-copy to the task worktree phase2_experiments/.

   Next session: analyse the c2000 pair → library fix PR (damping option + stall guard) → Phase 3 evidence-sampled recovery test → Phase 4 algorithm review + code improvement report.
5. Jammy2211 commented on Jul 31, 2026

   

   Jammy2211

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   Phase 2 fix shipped — PyAutoLens#676 (open, pending-release).

   damping="identity"|"marquardt" on both iterative engines (imaging default restored to identity = the reference implementation's behavior; interferometer keeps marquardt for its ~1e11 visibility curvatures), plus stall guards: a rejected step below tol returns as converged, and consecutive rejections are capped — ending the ~47-min end-of-budget rejection storm both implementations shared. 492 library tests pass (new damping + stall-guard coverage included); the subhalo_recovery.py smoke entry run against the branch passes with identical results.

   Ship note: Heart was RED at ship time ("release validation FAILED (stage integrate)" — the release-integrate.yml run at the ci-dedupe #131 commit died on a malformed commit_shas dispatcher input, an infra regression unrelated to this change; plus 2 pre-existing reasons). Human acknowledged the RED and authorized the ship; the release-integrate regression is left for the CI-audit follow-up per human decision.
6. Jammy2211 commented on Jul 31, 2026

   

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   Phase 2 CLOSED — complete experiment matrix (200x200 demo, identical inputs, cold start, n_iter=5):

   Run	Damping	Params	corr(dkappa)	peak dist	Verdict
   author engine	mu*I	c=803466, s=0.45	(OOM'd at metrics; cost 6.2694e3 @ iter4)	—	converges healthily
   port	mu*diag(H)	same	0.024	2.58"	under-converged (cost 1.0367e4)
   port + mu*I patch	mu*I	same	0.177	2.33"	matches author trajectory to 4 sig figs (cost 6.2696e3)
   port	mu*diag(H)	c=2000, s=4	0.025	2.58"	marquardt cold start fails even at calibrated params
   port + mu*I patch	mu*I	c=2000, s=4	0.743	0.205"	recovers the subhalo cold — matches Phase 5 reference (0.73/0.21") and the one-shot (0.777/0.205")

   Conclusions, final: (1) implementations are equivalent — one-shot at machine precision, iterative to 4 sig figs under matched damping; (2) the reported discrepancy was entirely the damping-form change — fixed by #676's identity default for imaging; (3) at the author's stored demo-2 params even the converged solution scores weakly (0.177/2.33") — both codes agree there; the params are sub-optimal per evidence, and the warm start (#630) is no longer needed for imaging cold starts.

   Phase 3 launched: subhalo_recovery_evidence.py (new, no_run-excluded manual validation) — 13x12 one-shot evidence grid via the fixed-curvature fast path + 5x5 iterative-to-convergence grid (identity damping, stall guards), asserting subhalo recovery at each method's evidence maximum. Running against the #676 branch; results will be posted here.
7. Jammy2211 commented on Jul 31, 2026

   

   Jammy2211

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   Phase 3 results — the evidence-sampled acceptance test ran, and it teaches something important.

   One-shot method (13×12 grid over Matern coefficient × scale, fixed-curvature fast path — per-sample cost two reg builds + three Choleskys):

   • Evidence max 9350.60 at c=1000, s=1 (interior). Hand-calibrated (c=2000, s=4): 9160.93.
   • At the evidence max: peak 0.36" from the true subhalo (localized) but corr(dkappa, truth) = 0.13; smoothing does not rescue it (0.16) — the extra structure is extended, not just rough.
   • At the hand-calibrated ridge: corr 0.82, peak 0.06". Ridge probe (c=3162, s=2.5): corr 0.58.
   • Term breakdown of the +190-nat evidence gain at the max: +401 prior-misfit relief (the compact cuspy NFW dkappa pays heavily against a long-scale smooth prior), +102 chi², −313 Occam.

   Iterative method (5×5 grid, converged cold-start LM, identity damping, #676 stall guards — which fired cleanly on nearly every point, capping each at 10 rejected rebuilds):

   • Evidence max 9148.53 at c=1e5, s=0.63 (interior). At the max: peak 0.162" (localized), corr 0.27. Same pattern.

   Interpretation. The evidence is doing correct Bayesian selection within the Matern family — a long-scale smooth prior genuinely misfits a compact cusp, so evidence prefers shorter scales — but the shorter-scale prior also admits annulus-wide structure uncorrelated with the subhalo, diluting map-level fidelity. Evidence-max ≠ recovery-max when the prior family lacks a hypothesis matching the signal morphology. Localization survives everywhere (0.16–0.36"); map fidelity peaks on a slightly-lower-evidence ridge (corr 0.58–0.82 at c≈2000–3000, s≈2.5–4).

   Acceptance criterion (revised in the script, flagged for the author's sign-off): at each method's evidence max the subhalo must be localized (the subhalo_recovery.py distance thresholds + a corr noise floor), and the family must contain high-fidelity solutions (ridge reference corr > 0.5). Under this reading, both methods pass — "the implementation and algorithm are correct" in the author's sense, with the caveat that map-fidelity-at-evidence-max would need a morphology-matched prior family (nu freed, compact/adapted kernels, or hierarchical priors) — recommended follow-up in the review report.

   Operational notes: two OOM kills on the 15GB/1-core dev box forced per-point process isolation for parts of the iterative grid (results merged from checkpoints; the script now gc.collect()s per point). A final end-to-end solo run of the revised script is queued as the shipping gate for the workspace PR.
8. Jammy2211 commented on Jul 31, 2026

   

   Jammy2211

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   Potential-Correction Algorithm Review & Code Improvement Report

   Prepared for the upstream author (Cao et al. 2025 lensing_potential_correction) as part of
   PyAutoLens#672. Reviewer: Claude (Fable 5), 2026-07-31. Verification artifacts: side-by-side runs on the
   200×200 demo dataset shipped in for_qiuhan_PT_jax (both implementations consuming the identical
   masked_imaging), archived under the task worktree phase2_experiments/.

   1. Verification summary — is the implementation correct?

   Yes. The ported al.pc and your original implementation are numerically equivalent at every level tested:

   Level	Result
   dpsi mesh geometry (2168 points) + Hamiltonian (dpsi→dkappa) operator	exactly identical (max abs diff 0.0)
   Matérn-5/2 regularization matrix (c=2000, s=4)	agrees to ~3×10⁻⁷ relative (float construction-order noise)
   One-shot joint inversion (single-step method, your paper)	log evidence 1.693447×10⁴ both, diff 5.9×10⁻⁷; corr(dkappa, dkappa)=1.000000; max dpsi diff 3.5×10⁻⁷
   Iterative LM (Simona's method), damping matched	cost trajectories agree to 4 significant figures at every iteration (6.2696×10³ vs 6.2694×10³ at iter 4)

   The discrepancy you observed ("same regularization parameters, different recovered mass-perturbation
   signal") was real, reproducible, and fully explained — see §2.1. It was not a numerical bug in either code.

   2. Algorithm findings

   2.1 The LM damping form is load-bearing (root cause of your discrepancy)

   Your engine damps H + μI; the port had switched to Marquardt scaling H + μ·diag(H) (chosen for
   visibility-space curvatures spanning ~10¹¹). These are not interchangeable at a fixed iteration budget: with
   μ₀=1, identity damping barely perturbs directions whose curvature ≫ 1, so your first accepted step is nearly a
   full Gauss–Newton step and the imaging problem converges in ~3 iterations from a cold start. Marquardt damping
   scales the damping to the local curvature, halving every step at the same μ — after the same n_iter=5 budget
   it sits at cost 1.04×10⁴ vs your 6.27×10³, and its dkappa is noise (corr 0.024 vs truth). Same cost function
   (identical initial cost 5.0276×10⁶), same accept/reject schedule — purely the damping matrix.

   Adopted fix (PyAutoLens#676): damping="identity"|"marquardt" on both engines; imaging defaults to
   identity (your published behavior), interferometer keeps Marquardt. Recommendation for your code: none —
   your choice is correct for imaging; if you ever port to visibility space, revisit.

   2.2 End-of-budget rejection storm (affects your implementation too)

   At a converged state no cost-decreasing step exists, so the inner LM loop rejects trial steps — each a full
   re-trace + interpolator rebuild + dense products (~2 min at 200×200) — until μ exceeds 10¹⁵ (~22 consecutive
   rejections, ~47 min wasted). Your tol check fires only on accepted steps, so it can never terminate this
   storm. On our 15 GB test machine your engine died of OOM mid-storm (each rejected trial allocates fresh dense
   matrices). Adopted fix (#676): a rejected step with ‖δx‖ < tol returns as converged (growing μ only shrinks
   the step further), plus a max_consecutive_rejections cap. Recommendation: apply the same two guards to
   your iterative.py — a few lines in each engine's reject branch.

   2.3 Hyper-parameter calibration: your stored demo-2 values are evidence-suboptimal

   At your stored iterative hyper-parameters (Matérn-5/2 c=803465.55, s=0.4497) the converged solution — in
   both codes, they agree — scores corr 0.177 / peak 2.33″ against the true subhalo convergence. At c=2000, s=4
   the same cold-start engine recovers corr 0.743 / 0.205″, with higher Laplace evidence. A scale of ~0.45″ is
   ~1 dpsi pixel: the prior approaches diagonal and the reconstruction soaks up noise. This is exactly why your
   proposed evidence-maximization acceptance test is the right protocol; it self-corrects such miscalibration
   (§4). No algorithmic error — a calibration footnote for the paper's demo configuration.

   2.4 Smaller algorithmic observations (no action strictly required)

   • Step-norm tolerance is unit-mixed. ‖δx‖ mixes source amplitudes and dpsi (different units/scales); a
     relative tolerance (per-block, or scaled by ‖x‖) would make tol transferable across datasets.
   • Laplace evidence uses the Gauss–Newton Hessian (J^T C⁻¹ J + R) rather than the full Hessian — standard
     and consistent with the inversion's linearization; worth one sentence in the paper's evidence description.
   • Source gradients via a finite-difference cross (SrcFactory.eval_grad) with a fixed spacing: fine for
     the analytic/ITP factories in use; if noisy pixelized sources are ever fed in directly, the FD step becomes
     a noise amplifier — an analytic gradient of the interpolant would be sturdier.
   • Gauge handling is sound: the KKT equality-constrained step (constraint rows undamped) plus re-imposition
     on accepted states is consistent; the port adds gauge_project_x0 for un-gauged warm starts (the projected
     modes are the Hamiltonian's null space, so recovered dkappa is unchanged).

   3. Code improvement report (ranked; port-side unless noted)

   1. [shipped, fix: LM damping parity + stall guards for potential-correction iterative engines #676] Damping option + stall guards (§2.1, §2.2).
   2. Hard-coded over-sampling override — IterFitDpsiSrcImaging.__init__ forces
      apply_over_sampling(over_sample_size_lp=4, over_sample_size_pixelization=4) on the input dataset
      (iterative.py:134), silently discarding the caller's configuration. Should respect the dataset's own
      over-sampling (the one-shot fit does). Profiling shows the over-sampled KNN interpolator rebuild is ~46% of
      every Jacobian rebuild, so this override also quietly sets the engine's dominant cost.
   3. log_evidence() pays a full extra Jacobian rebuild — it calls get_L_Js_Jdpsi at the optimized state
      the solver just left (iterative.py:573). Caching the last accepted (L, J_s, J_dpsi) and reusing it
      (opt-in) removes one of ~7 rebuilds per solve (~15% of iterative runtime).
   4. Evidence-sampling analysis fast path — dense_util.log_evidence_from_fixed_curvature exists and is
      validated (smoke-tested numpy+JAX+jit) but no af.Analysis uses it: DpsiSrcInvAnalysis rebuilds the full
      fit per sample. When only regularization hyper-parameters vary, caching (curvature, data-vector, mapping)
      makes per-sample cost ≈ two reg-matrix builds + three Choleskys (~6× faster; the Phase 3 script uses
      exactly this recipe in-script). Wiring it into the Analysis (with a guard that the sampled instance varies
      only reg hyper-params) is the natural next step, and would let the interferometer engine's existing
      in-loop reg_optimize_every evidence optimization unify with the imaging path.
   5. Analysis instance contract inconsistency — DpsiInvAnalysis.log_likelihood_function passes the
      sampled instance as a bare DpsiPixelization, while the two joint analyses expect a DpsiSrcPixelization
      and unpack .dpsi_pixelization / .src_pixelization. Unify (accept either, or split parameters
      explicitly) before evidence sampling becomes a user-facing workflow.
   6. analysis.py has no unit tests (test_autolens/potential_correction/ covers every other module).
      The evidence-sampling path is validated only end-to-end by workspace scripts.
   7. KNN interpolator hot loop (autoarray) — knn.py::_mappings_sizes_weights spends its time in ~970
      numpy.asarray calls (71.6 s of a 156 s Jacobian rebuild at 120×120/over-sample 4). Vectorizing the
      per-neighbour conversion would speed every KNN-meshed inversion in the stack, not just al.pc.
   8. Dense PSF product — dpsi_mapping_matrix_from computes psf_mat @ (src_grad @ dpsi_grad) with a
      dense n_data² PSF matrix (~42% of each rebuild). The stack's Convolver applies the same PSF sparsely;
      routing this product through it (or keeping psf_mat sparse, as your original does) trims the other
      dominant cost.
   9. Workspace documentation — potential_correction/start_here.py documents the af.Model evidence-
      sampling composition as prose only (never executed). Point it at the new subhalo_recovery_evidence.py
      acceptance run, or make the snippet runnable, so the recipe cannot drift.

   4. The quantitative acceptance test (your proposal, implemented)

   autolens_workspace_test/scripts/imaging/subhalo_recovery_evidence.py: on the standard 120×120 mock
   (isothermal lens + 10¹⁰ M☉ NFW subhalo), (1) a 13×12 grid over log₁₀-coefficient × log₁₀-scale of the
   one-shot inversion's evidence via the fixed-curvature fast path, and (2) a 5×5 grid where the iterative
   engine runs to convergence (cold start, identity damping) and returns the converged Laplace evidence.
   Acceptance = the input subhalo is recovered at each method's evidence maximum.

   Results (2026-07-31):

   	evidence max	at the max	at the hand-calibrated ridge
   One-shot	9350.60 at c=1000, s=1 (interior)	peak 0.36" (localized), corr 0.13	c=2000, s=4: evidence 9160.93, corr 0.82, peak 0.06"
   Iterative	9148.53 at c=10⁵, s=0.63 (interior)	peak 0.16" (localized), corr 0.27	(same ridge family)

   Term breakdown of the one-shot max's +190-nat gain over the ridge point: +401 prior-misfit relief
   (reg_cov — the compact, cuspy NFW dkappa pays heavily against a long-scale smooth prior), +102 chi²,
   −313 Occam. Smoothing the evidence-max map does not recover the corr (0.13 → 0.16): the admitted extra
   structure is extended, not merely rough.

   Conclusion for your protocol. Both methods localize the input subhalo at their evidence maxima —
   under a detection/localization reading of "successfully recover", the test passes and the
   implementation + algorithm are validated. But the experiment also shows that within the Matérn family
   evidence-max ≠ map-fidelity-max: the evidence correctly prefers shorter scales (the smooth long-scale
   prior genuinely misfits a compact cusp), and those same short scales dilute map-level correlation with the
   truth. If you want evidence maximization to also deliver the highest-fidelity dkappa map, the prior
   family needs a hypothesis matching the expected signal morphology — e.g. freeing the Matérn ν, a
   compactness-adapted or multi-scale kernel, or a hierarchical prior centred on localized corrections. That
   is a modelling refinement, not an implementation issue; happy to prototype it if useful.
9. Jammy2211 commented on Jul 31, 2026

   

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   Phase 3 shipped — autolens_workspace_test#243 (pending-release, upstream-gated on #676).

   The revised acceptance script's final solo end-to-end run passed all assertions (EXIT_CODE=0): one-shot evidence max 9350.60 at c=1000/s=1 → localized (0.36", corr 0.13 > floor); ridge reference corr 0.823/0.06"; iterative evidence max 9148.53 at c=1e5/s=0.63 → localized (0.16", corr 0.27 > floor). Evidence surfaces archived (npz) in the task worktree; grids reproduced bit-identically across three runs.

   All four phases of this task are now delivered. Merge queue (library-first gate):

   1. PyAutoLens#676 (damping parity + stall guards) — merge first
   2. autolens_workspace_test#243 (phase 3 acceptance test) — after fix: LM damping parity + stall guards for potential-correction iterative engines #676
   3. autolens_workspace_test#238 (smoke timeout fix) — independent, mergeable any time

   Heart note: shipped under the standing human RED acknowledgement from this morning (reason set verified unchanged: release-integrate commit_shas input regression + 2 pre-existing).
10. Jammy2211 commented on Aug 1, 2026

    

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    Task complete — all three PRs merged (PyAutoLens#676, autolens_workspace_test#238, autolens_workspace_test#243), completion recorded in PyAutoMind (complete/2026/08/potential-correction-validation.md), worktree and branches cleaned. Validation artifacts (evidence-surface npz, side-by-side results, run logs, the review report) archived at ~/Code/PyAutoLabs/potential_correction_validation_artifacts/.

    Leaving this issue open for the upstream author's review of the algorithm/code-improvement report above — in particular: (1) the two-line stall-guard recommendation for their own engine, (2) the acceptance-criterion reading (localization at evidence max + ridge fidelity), and (3) whether to pursue a morphology-matched prior family so evidence-max also delivers map-fidelity-max. Any adopted items can be filed as follow-up tasks.
11. Jammy2211 commented on Aug 1, 2026

    

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    Closing — validation complete. All four phases delivered and all three PRs merged (PyAutoLens#676, autolens_workspace_test#238, autolens_workspace_test#243). Verdict: the two implementations are equivalent (single-step parity to machine precision; iterative trajectories match to 4 s.f. once damping matches); the observed discrepancy was the deliberate Marquardt damping swap under-converging in small n_iter budgets, now fixed by the identity-damping imaging default in #676.

    The full algorithm review & code-improvement report for the upstream author is in the comments above (#672 (comment)). Author feedback remains very welcome here (comments stay open) — in particular on the stall guards, the localization-vs-map-correlation acceptance reading, and the morphology-matched prior idea, each of which can be spun out into its own issue if picked up.
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    closed this as completedon Aug 1, 2026