diff --git a/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.json b/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.json
index dd5c0a771..0f61a147a 100644
--- a/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.json
+++ b/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.json
@@ -12,7 +12,7 @@
   "cache_fresh": true,
   "provenance": {
    "provenance_schema": 1,
-   "captured_at": "2026-09-30T10:35:46Z",
+   "captured_at": "2026-09-30T16:55:21Z",
    "host": "DESKTOP-H143S82",
    "slurm": {
     "job_id": null,
@@ -20,23 +20,23 @@
     "array_task_id": null
    },
    "loadavg_at_import": [
-    1.927,
-    1.889,
-    1.698
+    4.929,
+    1.929,
+    1.171
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    "loadavg_at_write": [
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-    1.698
+    4.929,
+    1.929,
+    1.171
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-   "profiling_revision": "8adb629d658a143498ae2555ab1601dc72dca032",
+   "profiling_revision": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
    "library_revisions": {
-    "autolens_profiling": "8adb629d658a143498ae2555ab1601dc72dca032",
+    "autolens_profiling": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
     "PyAutoNerves": "1ec1c829acec0ce85a7eddf791eb66574d507884",
-    "PyAutoFit": "3fa8cf7f26dd51c9d00199f37176cc68ffb48356",
-    "PyAutoArray": "d4298445e8fc2c93be4b9312b480808b05047805",
-    "PyAutoGalaxy": "4900e6809101aa9e40ce40dc1bfa0c89874939ee",
-    "PyAutoLens": "092897e48adaed46c00574032bbdb59d340d57e0"
+    "PyAutoFit": "b13169e2ced2c44ed2b4a43f3a6ef1c92df989d4",
+    "PyAutoArray": "7a89e19a09760a0daf22f40e8b111b5388767f73",
+    "PyAutoGalaxy": "4c834cedee64a6fe530c7ac66d77de21cca346e8",
+    "PyAutoLens": "efd13c4cdfb7ab0d4a37549468998e06e31f27d7"
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    "library_versions": {
     "PyAutoNerves": "2026.8.17.1",
@@ -178,7 +178,7 @@
    "backend": "numpy"
   },
   "pdip_raw": {
-   "description": "Released default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library returns y = x / D and the reconstruction y * D, reproduced exactly. Extras: the backward-pass polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
+   "description": "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and interior) \u2014 reconstruction y * D, reproduced exactly. converged / iterations are the raw forward solve's (the polish is not counted). Extras: the polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward (+ raw_forward_backward_status)",
    "backend": "jax"
   },
@@ -246,26 +246,26 @@
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    "max_iterations": 20,
-   "median_wall_ms": 1.0920000004261965
+   "median_wall_ms": 2.616199999465607
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-   "median_flux_inactive_rel": 3.162506498913586e-05,
-   "worst_active_set_mismatch": 26,
-   "worst_objective_gap": 2.6084263568677235e-12,
-   "worst_kkt_residual_scaled": 2.6623732310776797e-14,
+   "worst_amp_rel_max": 503497921.96250796,
+   "median_amp_rel_max": 5.2851722677025943e-11,
+   "worst_amp_rel_max_sig": 0.2186910396320344,
+   "worst_amp_rel_l2": 0.00013209142455026892,
+   "worst_flux_rel_all": 0.0003309317052181464,
+   "worst_flux_rel_source": 0.049615932161212996,
+   "worst_flux_inactive_rel": 0.0003309329699063631,
+   "median_flux_inactive_rel": 1.68875786688709e-07,
+   "worst_active_set_mismatch": 23,
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+   "worst_kkt_residual_scaled": 3.2373436135630984e-16,
    "median_iterations": 18.0,
    "max_iterations": 24,
-   "median_wall_ms": 0.9911999986798037
+   "median_wall_ms": 1.5423000004375353
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   "pdip_raw_polish": {
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@@ -284,7 +284,7 @@
    "worst_kkt_residual_scaled": 3.2373436135630984e-16,
    "median_iterations": 23.0,
    "max_iterations": 30,
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@@ -303,7 +303,7 @@
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+   "median_wall_ms": 3.1761999998707324
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@@ -322,7 +322,7 @@
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+   "median_wall_ms": 2.198800000769552
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@@ -341,7 +341,7 @@
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    "max_iterations": 30,
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+   "median_wall_ms": 2.0376000029500574
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@@ -360,7 +360,7 @@
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@@ -379,7 +379,7 @@
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    "max_iterations": 200,
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@@ -398,7 +398,7 @@
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+   "median_wall_ms": 1.8887999976868741
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@@ -417,7 +417,7 @@
    "worst_kkt_residual_scaled": 3.2373144869487393e-16,
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@@ -436,7 +436,7 @@
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    "max_iterations": 50,
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@@ -463,7 +463,7 @@
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@@ -488,7 +488,7 @@
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@@ -513,7 +513,7 @@
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@@ -538,7 +538,7 @@
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@@ -563,7 +563,7 @@
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@@ -588,7 +588,7 @@
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@@ -613,7 +613,7 @@
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@@ -638,7 +638,7 @@
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@@ -663,7 +663,7 @@
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@@ -688,7 +688,7 @@
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@@ -713,7 +713,7 @@
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@@ -738,7 +738,7 @@
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@@ -763,7 +763,7 @@
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@@ -788,7 +788,7 @@
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@@ -813,7 +813,7 @@
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@@ -838,7 +838,7 @@
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@@ -863,7 +863,7 @@
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@@ -888,7 +888,7 @@
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@@ -913,7 +913,7 @@
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@@ -938,7 +938,7 @@
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-   "wall_ms": 1.0927000002993736
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@@ -963,7 +963,7 @@
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@@ -988,7 +988,7 @@
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-   "wall_ms": 1.1623999998846557
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@@ -1013,7 +1013,7 @@
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@@ -1038,7 +1038,7 @@
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@@ -1063,7 +1063,7 @@
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@@ -1088,7 +1088,7 @@
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@@ -1113,7 +1113,7 @@
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@@ -1138,7 +1138,7 @@
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@@ -1163,7 +1163,7 @@
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@@ -1188,7 +1188,7 @@
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@@ -1213,7 +1213,7 @@
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@@ -1238,7 +1238,7 @@
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@@ -1263,7 +1263,7 @@
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@@ -1288,7 +1288,7 @@
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@@ -1313,7 +1313,7 @@
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@@ -1338,7 +1338,7 @@
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@@ -1363,7 +1363,7 @@
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@@ -1388,7 +1388,7 @@
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@@ -1413,7 +1413,7 @@
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@@ -1438,7 +1438,7 @@
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@@ -1463,7 +1463,7 @@
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@@ -1488,7 +1488,7 @@
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@@ -1513,7 +1513,7 @@
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@@ -1538,7 +1538,7 @@
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@@ -1563,7 +1563,7 @@
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@@ -1588,7 +1588,7 @@
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@@ -1613,7 +1613,7 @@
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@@ -1638,7 +1638,7 @@
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@@ -1663,7 +1663,7 @@
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@@ -1688,7 +1688,7 @@
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@@ -1713,7 +1713,7 @@
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@@ -2841,25 +2841,25 @@
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@@ -3030,25 +3030,25 @@
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@@ -3111,25 +3111,25 @@
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@@ -3138,25 +3138,25 @@
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@@ -3165,25 +3165,25 @@
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@@ -3192,25 +3192,25 @@
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@@ -3219,25 +3219,25 @@
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@@ -3246,25 +3246,25 @@
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@@ -3273,25 +3273,25 @@
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@@ -3300,25 +3300,25 @@
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@@ -3489,25 +3489,25 @@
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@@ -3516,25 +3516,25 @@
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@@ -3543,25 +3543,25 @@
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@@ -3570,25 +3570,25 @@
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@@ -3597,25 +3597,25 @@
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@@ -3624,25 +3624,25 @@
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@@ -3678,25 +3678,25 @@
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@@ -3705,25 +3705,25 @@
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@@ -3732,25 +3732,25 @@
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@@ -3759,25 +3759,25 @@
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@@ -3786,25 +3786,25 @@
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@@ -3813,25 +3813,25 @@
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@@ -3840,25 +3840,25 @@
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@@ -3867,25 +3867,25 @@
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@@ -3894,25 +3894,25 @@
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@@ -3921,25 +3921,25 @@
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@@ -3948,25 +3948,25 @@
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@@ -4650,7 +4650,7 @@
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@@ -4731,7 +4731,7 @@
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@@ -4812,7 +4812,7 @@
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@@ -4839,7 +4839,7 @@
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@@ -4866,7 +4866,7 @@
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@@ -5028,7 +5028,7 @@
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@@ -5217,7 +5217,7 @@
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@@ -5271,7 +5271,7 @@
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@@ -5325,7 +5325,7 @@
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@@ -5433,7 +5433,7 @@
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@@ -5460,7 +5460,7 @@
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@@ -5514,7 +5514,7 @@
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@@ -5541,7 +5541,7 @@
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@@ -5568,7 +5568,7 @@
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@@ -5595,7 +5595,7 @@
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@@ -5622,7 +5622,7 @@
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@@ -5703,7 +5703,7 @@
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@@ -5730,7 +5730,7 @@
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@@ -5811,7 +5811,7 @@
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@@ -5865,7 +5865,7 @@
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@@ -5892,7 +5892,7 @@
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@@ -5919,7 +5919,7 @@
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@@ -5946,7 +5946,7 @@
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@@ -6000,7 +6000,7 @@
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@@ -6054,7 +6054,7 @@
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@@ -6513,7 +6513,7 @@
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    "stats": {},
-   "wall_ms": 2.269299999170471
+   "wall_ms": 4.280900000594556
   },
   {
    "system": "slam_spread_hst/sigma_min_x2_v16",
@@ -21551,7 +21551,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.2456000006059185
+   "wall_ms": 3.21319999784464
   },
   {
    "system": "slam_spread_hst/sigma_min_x2_v24",
@@ -21573,7 +21573,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.2962000002735294
+   "wall_ms": 3.1273999993572943
   },
   {
    "system": "slam_spread_hst/sigma_min_x2_v32",
@@ -21595,7 +21595,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.5212000000465196
+   "wall_ms": 4.669299996749032
   },
   {
    "system": "slam_spread_hst/sigma_min_x2_v40",
@@ -21617,7 +21617,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.3855999988882104
+   "wall_ms": 4.018600004201289
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v00",
@@ -21639,7 +21639,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.723700001297402
+   "wall_ms": 4.684300001827069
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v08",
@@ -21661,7 +21661,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.422500001557637
+   "wall_ms": 5.280899997160304
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v16",
@@ -21683,7 +21683,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.6151000001846114
+   "wall_ms": 3.813399998762179
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v24",
@@ -21705,7 +21705,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.5151999998342944
+   "wall_ms": 4.020799999125302
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v32",
@@ -21727,7 +21727,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 3.034999999727006
+   "wall_ms": 3.280800003267359
   },
   {
    "system": "slam_spread_hst/noise_x0p3_v40",
@@ -21749,7 +21749,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.877799999623676
+   "wall_ms": 3.105600000708364
   },
   {
    "system": "slam_spread_hst/noise_x3_v00",
@@ -21771,7 +21771,7 @@
    "converged": true,
    "iterations": 48,
    "stats": {},
-   "wall_ms": 2.680599998711841
+   "wall_ms": 2.8268000023672357
   },
   {
    "system": "slam_spread_hst/noise_x3_v08",
@@ -21793,7 +21793,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.7258999998593936
+   "wall_ms": 3.2040000005508773
   },
   {
    "system": "slam_spread_hst/noise_x3_v16",
@@ -21815,7 +21815,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.6906000002782093
+   "wall_ms": 4.067499998200219
   },
   {
    "system": "slam_spread_hst/noise_x3_v24",
@@ -21837,7 +21837,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.6902999998128507
+   "wall_ms": 4.094500000064727
   },
   {
    "system": "slam_spread_hst/noise_x3_v32",
@@ -21859,7 +21859,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.5783000000956235
+   "wall_ms": 4.65740000072401
   },
   {
    "system": "slam_spread_hst/noise_x3_v40",
@@ -21881,7 +21881,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.5925000009010546
+   "wall_ms": 4.988100001355633
   },
   {
    "system": "euclid_vis_lp/euclid_vis_lp_k0",
@@ -21903,7 +21903,7 @@
    "converged": false,
    "iterations": 50,
    "stats": {},
-   "wall_ms": 2.6133999999728985
+   "wall_ms": 4.168599996773992
   }
  ]
 }
diff --git a/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.png b/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.png
index 1b3f3b146..12ccc4393 100644
Binary files a/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.png and b/results/lens/solver/accuracy_posthoc_summary_all_v2026.8.17.1.png differ
diff --git a/results/lens/solver/accuracy_summary_all_v2026.8.17.1.json b/results/lens/solver/accuracy_summary_all_v2026.8.17.1.json
index 14d38252c..3885cd17f 100644
--- a/results/lens/solver/accuracy_summary_all_v2026.8.17.1.json
+++ b/results/lens/solver/accuracy_summary_all_v2026.8.17.1.json
@@ -12,7 +12,7 @@
   "cache_fresh": true,
   "provenance": {
    "provenance_schema": 1,
-   "captured_at": "2026-09-30T10:35:10Z",
+   "captured_at": "2026-09-30T16:54:28Z",
    "host": "DESKTOP-H143S82",
    "slurm": {
     "job_id": null,
@@ -20,23 +20,23 @@
     "array_task_id": null
    },
    "loadavg_at_import": [
-    2.13,
-    1.926,
-    1.703
+    1.756,
+    1.155,
+    0.901
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    "loadavg_at_write": [
-    2.13,
-    1.926,
-    1.703
+    1.756,
+    1.155,
+    0.901
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-   "profiling_revision": "8adb629d658a143498ae2555ab1601dc72dca032",
+   "profiling_revision": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
    "library_revisions": {
-    "autolens_profiling": "8adb629d658a143498ae2555ab1601dc72dca032",
+    "autolens_profiling": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
     "PyAutoNerves": "1ec1c829acec0ce85a7eddf791eb66574d507884",
-    "PyAutoFit": "3fa8cf7f26dd51c9d00199f37176cc68ffb48356",
-    "PyAutoArray": "d4298445e8fc2c93be4b9312b480808b05047805",
-    "PyAutoGalaxy": "4900e6809101aa9e40ce40dc1bfa0c89874939ee",
-    "PyAutoLens": "092897e48adaed46c00574032bbdb59d340d57e0"
+    "PyAutoFit": "b13169e2ced2c44ed2b4a43f3a6ef1c92df989d4",
+    "PyAutoArray": "7a89e19a09760a0daf22f40e8b111b5388767f73",
+    "PyAutoGalaxy": "4c834cedee64a6fe530c7ac66d77de21cca346e8",
+    "PyAutoLens": "efd13c4cdfb7ab0d4a37549468998e06e31f27d7"
    },
    "library_versions": {
     "PyAutoNerves": "2026.8.17.1",
@@ -180,22 +180,22 @@
    "backend": "jax"
   },
   "pdip_raw": {
-   "description": "Released default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library returns y = x / D and the reconstruction y * D, reproduced exactly. Extras: the backward-pass polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
+   "description": "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and interior) \u2014 reconstruction y * D, reproduced exactly. converged / iterations are the raw forward solve's (the polish is not counted). Extras: the polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward (+ raw_forward_backward_status)",
    "backend": "jax"
   },
   "pdip_raw_tol_1e-1": {
-   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-1 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-1 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly.",
+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-1 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-1 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
    "backend": "jax"
   },
   "pdip_raw_tol_1e-2": {
-   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-2 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-2 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly.",
+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-2 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-2 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
    "backend": "jax"
   },
   "pdip_raw_tol_1e-3": {
-   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-3 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-3 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly.",
+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-3 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-3 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
    "backend": "jax"
   },
@@ -233,7 +233,7 @@
    "worst_kkt_residual_scaled": 8.634465074549975e-16,
    "median_iterations": 9.0,
    "max_iterations": 20,
-   "median_wall_ms": 1.6323999989253934
+   "median_wall_ms": 1.1405000041122548
   },
   "pdip_jacobi": {
    "n_systems": 81,
@@ -252,26 +252,26 @@
    "worst_kkt_residual_scaled": 8.249141226631165e+61,
    "median_iterations": 19.0,
    "max_iterations": 50,
-   "median_wall_ms": 1.3230000004114117
+   "median_wall_ms": 1.2186999956611544
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   "pdip_raw": {
    "n_systems": 81,
    "n_unconverged": 0,
    "n_nonfinite": 0,
-   "worst_amp_rel_max": 62276811993.98223,
-   "median_amp_rel_max": 8.906530529447453e-08,
-   "worst_amp_rel_max_sig": 26.99362516994422,
-   "worst_amp_rel_l2": 0.04336774574740038,
-   "worst_flux_rel_all": 0.11522530752020606,
-   "worst_flux_rel_source": 16.355202018136207,
-   "worst_flux_inactive_rel": 0.11522530892329123,
-   "median_flux_inactive_rel": 3.162506498913586e-05,
-   "worst_active_set_mismatch": 26,
-   "worst_objective_gap": 2.6084263568677235e-12,
-   "worst_kkt_residual_scaled": 2.6623732310776797e-14,
+   "worst_amp_rel_max": 503497921.96250796,
+   "median_amp_rel_max": 5.2851722677025943e-11,
+   "worst_amp_rel_max_sig": 0.2186910396320344,
+   "worst_amp_rel_l2": 0.00013209142455026892,
+   "worst_flux_rel_all": 0.0003309317052181464,
+   "worst_flux_rel_source": 0.049615932161212996,
+   "worst_flux_inactive_rel": 0.0003309329699063631,
+   "median_flux_inactive_rel": 1.68875786688709e-07,
+   "worst_active_set_mismatch": 23,
+   "worst_objective_gap": 8.176910535652831e-16,
+   "worst_kkt_residual_scaled": 3.2373436135630984e-16,
    "median_iterations": 18.0,
    "max_iterations": 24,
-   "median_wall_ms": 1.2623999991774326
+   "median_wall_ms": 1.1591000002226792
   },
   "pdip_raw_tol_1e-1": {
    "n_systems": 81,
@@ -290,7 +290,7 @@
    "worst_kkt_residual_scaled": 1.8032308156002137e-13,
    "median_iterations": 16.0,
    "max_iterations": 23,
-   "median_wall_ms": 1.287400000364869
+   "median_wall_ms": 0.8681000035721809
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   "pdip_raw_tol_1e-2": {
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@@ -309,7 +309,7 @@
    "worst_kkt_residual_scaled": 2.6623732310776797e-14,
    "median_iterations": 18.0,
    "max_iterations": 24,
-   "median_wall_ms": 1.3290000006236369
+   "median_wall_ms": 0.9076000060304068
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   "pdip_raw_tol_1e-3": {
    "n_systems": 81,
@@ -328,7 +328,7 @@
    "worst_kkt_residual_scaled": 3.930374852211771e-15,
    "median_iterations": 19.0,
    "max_iterations": 25,
-   "median_wall_ms": 1.4104000001680106
+   "median_wall_ms": 0.9295999989262782
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   "pdip_raw_tol_jaxnnls": {
    "n_systems": 81,
@@ -347,7 +347,7 @@
    "worst_kkt_residual_scaled": 2.427672221859022e-16,
    "median_iterations": 50.0,
    "max_iterations": 50,
-   "median_wall_ms": 3.0728000001545297
+   "median_wall_ms": 2.1194000000832602
   },
   "pdip_raw_polish": {
    "n_systems": 81,
@@ -366,7 +366,7 @@
    "worst_kkt_residual_scaled": 3.2373436135630984e-16,
    "median_iterations": 23.0,
    "max_iterations": 30,
-   "median_wall_ms": 1.71299999965413
+   "median_wall_ms": 1.222899998538196
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   "certified": {
    "n_systems": 81,
@@ -385,7 +385,7 @@
    "worst_kkt_residual_scaled": 0.018128155696099987,
    "median_iterations": 16.0,
    "max_iterations": 16,
-   "median_wall_ms": 1.1474000002635876
+   "median_wall_ms": 0.7771999953547493
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  "rows": [
@@ -412,7 +412,7 @@
     "inner_iterations": 6,
     "n_passive": 26
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-   "wall_ms": 1.761100000294391
+   "wall_ms": 1.1721999981091358
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   {
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@@ -437,7 +437,7 @@
     "inner_iterations": 5,
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-   "wall_ms": 1.3225000002421439
+   "wall_ms": 0.8438000004389323
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@@ -462,7 +462,7 @@
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-   "wall_ms": 1.4733000007254304
+   "wall_ms": 0.9395000015501864
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@@ -487,7 +487,7 @@
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@@ -512,7 +512,7 @@
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@@ -537,7 +537,7 @@
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-   "wall_ms": 1.8629000005603302
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@@ -562,7 +562,7 @@
     "inner_iterations": 6,
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-   "wall_ms": 2.3110000001906883
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@@ -587,7 +587,7 @@
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-   "wall_ms": 2.2743000008631498
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    "system": "slam48_hst/v00",
@@ -612,7 +612,7 @@
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    },
-   "wall_ms": 2.391100000750157
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@@ -637,7 +637,7 @@
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-   "wall_ms": 3.305500000351458
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@@ -662,7 +662,7 @@
     "inner_iterations": 8,
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-   "wall_ms": 3.286099999968428
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@@ -687,7 +687,7 @@
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     "n_passive": 24
    },
-   "wall_ms": 2.0792999985133065
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@@ -712,7 +712,7 @@
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     "n_passive": 22
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-   "wall_ms": 1.7222000005858717
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@@ -737,7 +737,7 @@
     "inner_iterations": 4,
     "n_passive": 27
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-   "wall_ms": 1.189499998872634
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    "system": "slam48_hst/v06",
@@ -762,7 +762,7 @@
     "inner_iterations": 6,
     "n_passive": 21
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-   "wall_ms": 1.415800001268508
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    "system": "slam48_hst/v07",
@@ -787,7 +787,7 @@
     "inner_iterations": 6,
     "n_passive": 24
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-   "wall_ms": 2.3098000001482433
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    "system": "slam48_hst/v08",
@@ -812,7 +812,7 @@
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-   "wall_ms": 1.6323999989253934
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   {
    "system": "slam48_hst/v09",
@@ -837,7 +837,7 @@
     "inner_iterations": 6,
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-   "wall_ms": 1.3621000016428297
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@@ -862,7 +862,7 @@
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-   "wall_ms": 1.841499999500229
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   {
    "system": "slam48_hst/v11",
@@ -887,7 +887,7 @@
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-   "wall_ms": 1.4430999999603955
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    "system": "slam48_hst/v12",
@@ -912,7 +912,7 @@
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@@ -937,7 +937,7 @@
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    "system": "slam48_hst/v14",
@@ -962,7 +962,7 @@
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    "system": "slam48_hst/v15",
@@ -987,7 +987,7 @@
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@@ -4734,25 +4734,25 @@
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@@ -4761,25 +4761,25 @@
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@@ -4788,25 +4788,25 @@
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@@ -4815,25 +4815,25 @@
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@@ -4842,25 +4842,25 @@
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@@ -4869,25 +4869,25 @@
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@@ -4896,25 +4896,25 @@
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@@ -4923,25 +4923,25 @@
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@@ -5166,25 +5166,25 @@
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@@ -5220,25 +5220,25 @@
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@@ -5247,25 +5247,25 @@
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@@ -5382,25 +5382,25 @@
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@@ -5409,25 +5409,25 @@
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@@ -5436,25 +5436,25 @@
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@@ -5463,25 +5463,25 @@
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@@ -5490,25 +5490,25 @@
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@@ -5517,25 +5517,25 @@
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@@ -5544,25 +5544,25 @@
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@@ -5571,25 +5571,25 @@
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diff --git a/results/lens/solver/accuracy_summary_all_v2026.8.17.1.png b/results/lens/solver/accuracy_summary_all_v2026.8.17.1.png
index a2d67cfb7..6b1470a81 100644
Binary files a/results/lens/solver/accuracy_summary_all_v2026.8.17.1.png and b/results/lens/solver/accuracy_summary_all_v2026.8.17.1.png differ
diff --git a/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.json b/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.json
index caee4fffe..843e7ae04 100644
--- a/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.json
+++ b/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.json
@@ -12,7 +12,7 @@
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+   "captured_at": "2026-09-30T16:54:39Z",
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    "backend": "jax"
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   "pdip_raw": {
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+   "description": "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and interior) \u2014 reconstruction y * D, reproduced exactly. converged / iterations are the raw forward solve's (the polish is not counted). Extras: the polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward (+ raw_forward_backward_status)",
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   "pdip_raw_tol_1e-1": {
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+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-1 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-1 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
    "backend": "jax"
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   "pdip_raw_tol_1e-2": {
-   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-2 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-2 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly.",
+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-2 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-2 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
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+   "description": "Raw forward PDIP with the data-scaled tolerance at factor 1e-3 in place of DATA_SCALED_TOL_FACTOR (tol = 1e-3 * n * eps * max(1, max|q|), i.e. data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The 1e-2 row reproduces the released tolerance through solve_nnls directly, with no polish (so, since PyAutoArray#595, not pdip_raw's value).",
    "library_function": "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
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@@ -150,11 +150,14 @@
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@@ -166,27 +169,33 @@
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    "worst_flux_rel_source": 3.359896757899868e+70,
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+   "worst_amp_rel_max_sig": 4.953172552819985e-06,
+   "worst_amp_rel_l2": 4.2883367385145313e-07,
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+   "worst_flux_rel_source": 4.930492604392857e-05,
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@@ -198,11 +207,14 @@
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    "worst_flux_rel_all": 0.0001451538141043865,
    "worst_flux_rel_source": 0.016886471334553167,
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@@ -214,11 +226,14 @@
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    "worst_flux_rel_all": 5.359322145958006e-05,
    "worst_flux_rel_source": 0.006441797500967694,
+   "worst_flux_inactive_rel": 5.3593279625567716e-05,
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@@ -1518,7 +1661,7 @@
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@@ -1630,6 +1781,8 @@
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@@ -1643,7 +1796,7 @@
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@@ -1690,7 +1847,7 @@
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@@ -1778,7 +1943,7 @@
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@@ -1790,6 +1955,8 @@
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@@ -1800,7 +1967,7 @@
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@@ -1822,7 +1991,7 @@
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@@ -1834,6 +2003,8 @@
    "amp_rel_max_sig": 1.0,
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@@ -1844,7 +2015,7 @@
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  ]
 }
diff --git a/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.png b/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.png
index f0dc9d686..ecbfc4074 100644
Binary files a/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.png and b/results/lens/solver/accuracy_summary_slam_fixture_571_v2026.8.17.1.png differ
diff --git a/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.json b/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.json
index 5c908a5cd..d48f0882d 100644
--- a/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.json
+++ b/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.json
@@ -12,7 +12,7 @@
   "cache_fresh": true,
   "provenance": {
    "provenance_schema": 1,
-   "captured_at": "2026-09-30T10:35:22Z",
+   "captured_at": "2026-09-30T16:55:44Z",
    "host": "DESKTOP-H143S82",
    "slurm": {
     "job_id": null,
@@ -20,23 +20,23 @@
     "array_task_id": null
    },
    "loadavg_at_import": [
-    1.956,
-    1.896,
-    1.695
+    4.04,
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+    1.184
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    "loadavg_at_write": [
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-    1.896,
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-   "profiling_revision": "8adb629d658a143498ae2555ab1601dc72dca032",
+   "profiling_revision": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
    "library_revisions": {
-    "autolens_profiling": "8adb629d658a143498ae2555ab1601dc72dca032",
+    "autolens_profiling": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
     "PyAutoNerves": "1ec1c829acec0ce85a7eddf791eb66574d507884",
-    "PyAutoFit": "3fa8cf7f26dd51c9d00199f37176cc68ffb48356",
-    "PyAutoArray": "d4298445e8fc2c93be4b9312b480808b05047805",
-    "PyAutoGalaxy": "4900e6809101aa9e40ce40dc1bfa0c89874939ee",
-    "PyAutoLens": "092897e48adaed46c00574032bbdb59d340d57e0"
+    "PyAutoFit": "b13169e2ced2c44ed2b4a43f3a6ef1c92df989d4",
+    "PyAutoArray": "7a89e19a09760a0daf22f40e8b111b5388767f73",
+    "PyAutoGalaxy": "4c834cedee64a6fe530c7ac66d77de21cca346e8",
+    "PyAutoLens": "efd13c4cdfb7ab0d4a37549468998e06e31f27d7"
    },
    "library_versions": {
     "PyAutoNerves": "2026.8.17.1",
@@ -179,7 +179,7 @@
    100,
    200
   ],
-  "description": "Released default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library returns y = x / D and the reconstruction y * D, reproduced exactly. Extras: the backward-pass polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
+  "description": "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and interior) \u2014 reconstruction y * D, reproduced exactly. converged / iterations are the raw forward solve's (the polish is not counted). Extras: the polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
   "library_function": "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward"
  },
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@@ -357,7 +357,7 @@
    "converged": false,
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@@ -380,7 +380,7 @@
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@@ -403,7 +403,7 @@
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@@ -426,7 +426,7 @@
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@@ -449,7 +449,7 @@
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@@ -472,7 +472,7 @@
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@@ -771,7 +771,7 @@
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@@ -794,7 +794,7 @@
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@@ -817,7 +817,7 @@
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@@ -840,7 +840,7 @@
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@@ -863,7 +863,7 @@
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@@ -886,7 +886,7 @@
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@@ -894,22 +894,22 @@
    "group": "slam48_hst",
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@@ -932,7 +932,7 @@
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@@ -1001,7 +1001,7 @@
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@@ -1139,7 +1139,7 @@
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@@ -3623,7 +3623,7 @@
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@@ -3631,22 +3631,22 @@
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@@ -3669,7 +3669,7 @@
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@@ -3677,22 +3677,22 @@
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@@ -3715,7 +3715,7 @@
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@@ -3738,7 +3738,7 @@
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@@ -3761,7 +3761,7 @@
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@@ -3784,7 +3784,7 @@
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@@ -3807,7 +3807,7 @@
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@@ -3853,7 +3853,7 @@
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@@ -3907,22 +3907,22 @@
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@@ -3930,22 +3930,22 @@
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@@ -3953,22 +3953,22 @@
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@@ -3999,22 +3999,22 @@
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@@ -4022,22 +4022,22 @@
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@@ -4060,7 +4060,7 @@
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@@ -4091,22 +4091,22 @@
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@@ -4114,22 +4114,22 @@
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@@ -4160,22 +4160,22 @@
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@@ -4183,22 +4183,22 @@
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@@ -4206,22 +4206,22 @@
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@@ -4229,22 +4229,22 @@
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@@ -4252,22 +4252,22 @@
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@@ -4275,22 +4275,22 @@
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@@ -4298,22 +4298,22 @@
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@@ -4321,22 +4321,22 @@
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@@ -4367,22 +4367,22 @@
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@@ -4390,22 +4390,22 @@
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@@ -4413,22 +4413,22 @@
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@@ -4436,22 +4436,22 @@
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@@ -4459,22 +4459,22 @@
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@@ -4482,22 +4482,22 @@
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@@ -4505,22 +4505,22 @@
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@@ -4528,22 +4528,22 @@
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@@ -4551,22 +4551,22 @@
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@@ -4574,22 +4574,22 @@
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@@ -4597,22 +4597,22 @@
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@@ -4620,22 +4620,22 @@
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@@ -4643,22 +4643,22 @@
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@@ -4735,22 +4735,22 @@
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@@ -4781,22 +4781,22 @@
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@@ -4804,22 +4804,22 @@
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@@ -4827,22 +4827,22 @@
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@@ -4850,22 +4850,22 @@
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@@ -4873,22 +4873,22 @@
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@@ -4896,22 +4896,22 @@
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@@ -4919,22 +4919,22 @@
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@@ -4942,22 +4942,22 @@
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@@ -4988,22 +4988,22 @@
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@@ -5011,22 +5011,22 @@
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@@ -5034,22 +5034,22 @@
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@@ -5057,22 +5057,22 @@
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@@ -5080,22 +5080,22 @@
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@@ -5103,22 +5103,22 @@
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@@ -5126,22 +5126,22 @@
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@@ -5149,22 +5149,22 @@
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@@ -5172,22 +5172,22 @@
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@@ -5195,22 +5195,22 @@
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   {
@@ -5218,22 +5218,22 @@
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@@ -5241,22 +5241,22 @@
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@@ -5287,22 +5287,22 @@
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@@ -5310,22 +5310,22 @@
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@@ -5333,22 +5333,22 @@
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@@ -5379,22 +5379,22 @@
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@@ -5402,22 +5402,22 @@
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@@ -5425,22 +5425,22 @@
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@@ -5448,22 +5448,22 @@
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@@ -5471,22 +5471,22 @@
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@@ -5494,22 +5494,22 @@
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@@ -5517,22 +5517,22 @@
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@@ -5563,22 +5563,22 @@
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   {
@@ -5586,22 +5586,22 @@
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   {
@@ -5609,22 +5609,22 @@
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   {
@@ -5647,7 +5647,7 @@
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   {
@@ -5670,7 +5670,7 @@
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   {
@@ -5678,22 +5678,22 @@
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   {
@@ -5716,7 +5716,7 @@
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   {
@@ -5739,7 +5739,7 @@
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   {
@@ -5762,7 +5762,7 @@
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   {
@@ -5770,22 +5770,22 @@
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@@ -5793,22 +5793,22 @@
    "group": "slam_spread_hst",
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@@ -5816,22 +5816,22 @@
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@@ -5839,22 +5839,22 @@
    "group": "slam_spread_hst",
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   {
@@ -5862,22 +5862,22 @@
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   {
@@ -5885,22 +5885,22 @@
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   {
@@ -5923,7 +5923,7 @@
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   {
@@ -5931,22 +5931,22 @@
    "group": "slam_fixture_571",
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@@ -6092,22 +6092,22 @@
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@@ -6115,22 +6115,22 @@
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@@ -6161,22 +6161,22 @@
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@@ -6184,22 +6184,22 @@
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@@ -6207,22 +6207,22 @@
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@@ -6230,22 +6230,22 @@
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@@ -6276,22 +6276,22 @@
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@@ -6299,22 +6299,22 @@
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@@ -6322,22 +6322,22 @@
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@@ -6345,22 +6345,22 @@
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@@ -6368,22 +6368,22 @@
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@@ -6391,22 +6391,22 @@
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@@ -6414,22 +6414,22 @@
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@@ -6437,22 +6437,22 @@
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@@ -6460,22 +6460,22 @@
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@@ -6483,22 +6483,22 @@
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@@ -6506,22 +6506,22 @@
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@@ -6529,22 +6529,22 @@
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@@ -6575,22 +6575,22 @@
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@@ -6598,22 +6598,22 @@
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@@ -6621,22 +6621,22 @@
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@@ -6667,22 +6667,22 @@
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@@ -6690,22 +6690,22 @@
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@@ -6713,22 +6713,22 @@
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@@ -6736,22 +6736,22 @@
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@@ -6759,22 +6759,22 @@
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@@ -6782,22 +6782,22 @@
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@@ -6805,22 +6805,22 @@
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@@ -6828,22 +6828,22 @@
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@@ -6897,22 +6897,22 @@
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@@ -6920,22 +6920,22 @@
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@@ -6943,22 +6943,22 @@
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@@ -6966,22 +6966,22 @@
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@@ -6989,22 +6989,22 @@
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@@ -7012,22 +7012,22 @@
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@@ -7035,22 +7035,22 @@
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@@ -7058,22 +7058,22 @@
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@@ -7081,22 +7081,22 @@
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@@ -7150,22 +7150,22 @@
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@@ -7173,22 +7173,22 @@
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   {
@@ -7196,22 +7196,22 @@
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@@ -7219,22 +7219,22 @@
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@@ -7242,22 +7242,22 @@
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   {
@@ -7265,22 +7265,22 @@
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@@ -7288,22 +7288,22 @@
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@@ -7311,22 +7311,22 @@
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@@ -7334,22 +7334,22 @@
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@@ -7357,22 +7357,22 @@
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@@ -7380,22 +7380,22 @@
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@@ -7403,22 +7403,22 @@
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@@ -7449,22 +7449,22 @@
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@@ -7472,22 +7472,22 @@
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@@ -7495,22 +7495,22 @@
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@@ -7518,22 +7518,22 @@
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@@ -7541,22 +7541,22 @@
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@@ -7564,22 +7564,22 @@
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@@ -7587,22 +7587,22 @@
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@@ -7610,22 +7610,22 @@
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@@ -7633,22 +7633,22 @@
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@@ -7656,22 +7656,22 @@
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@@ -7679,22 +7679,22 @@
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@@ -7702,22 +7702,22 @@
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@@ -7725,22 +7725,22 @@
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@@ -7748,22 +7748,22 @@
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@@ -7771,22 +7771,22 @@
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@@ -7794,22 +7794,22 @@
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@@ -7817,22 +7817,22 @@
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@@ -7840,22 +7840,22 @@
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@@ -7863,22 +7863,22 @@
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@@ -7886,22 +7886,22 @@
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@@ -7909,22 +7909,22 @@
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@@ -7932,22 +7932,22 @@
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@@ -7955,22 +7955,22 @@
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@@ -7978,22 +7978,22 @@
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@@ -8001,22 +8001,22 @@
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@@ -8070,22 +8070,22 @@
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@@ -8093,22 +8093,22 @@
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@@ -8116,22 +8116,22 @@
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@@ -8139,22 +8139,22 @@
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@@ -8162,22 +8162,22 @@
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@@ -8185,22 +8185,22 @@
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@@ -8208,22 +8208,22 @@
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@@ -8231,22 +8231,22 @@
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@@ -8254,22 +8254,22 @@
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@@ -8300,22 +8300,22 @@
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@@ -8323,22 +8323,22 @@
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@@ -8346,22 +8346,22 @@
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@@ -8369,22 +8369,22 @@
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@@ -8392,22 +8392,22 @@
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@@ -8415,22 +8415,22 @@
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@@ -8438,22 +8438,22 @@
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@@ -8461,22 +8461,22 @@
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@@ -8484,22 +8484,22 @@
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@@ -8507,22 +8507,22 @@
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@@ -8530,22 +8530,22 @@
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@@ -8553,22 +8553,22 @@
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@@ -8576,22 +8576,22 @@
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@@ -8622,22 +8622,22 @@
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@@ -8645,22 +8645,22 @@
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@@ -8691,22 +8691,22 @@
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@@ -8714,22 +8714,22 @@
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@@ -8737,22 +8737,22 @@
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@@ -8760,22 +8760,22 @@
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@@ -8806,22 +8806,22 @@
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@@ -8829,22 +8829,22 @@
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@@ -8852,22 +8852,22 @@
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@@ -8875,22 +8875,22 @@
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@@ -8898,22 +8898,22 @@
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@@ -8921,22 +8921,22 @@
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@@ -8944,22 +8944,22 @@
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@@ -8967,22 +8967,22 @@
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@@ -8990,22 +8990,22 @@
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@@ -9013,22 +9013,22 @@
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@@ -9036,22 +9036,22 @@
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@@ -9059,22 +9059,22 @@
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   {
@@ -9082,22 +9082,22 @@
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   {
@@ -9105,22 +9105,22 @@
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   {
@@ -9128,22 +9128,22 @@
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@@ -9151,22 +9151,22 @@
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@@ -9174,22 +9174,22 @@
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@@ -9197,22 +9197,22 @@
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@@ -9220,22 +9220,22 @@
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@@ -9243,22 +9243,22 @@
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@@ -9266,22 +9266,22 @@
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@@ -9289,22 +9289,22 @@
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@@ -9312,22 +9312,22 @@
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@@ -9335,22 +9335,22 @@
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@@ -9358,22 +9358,22 @@
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@@ -9381,22 +9381,22 @@
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@@ -9404,22 +9404,22 @@
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@@ -9427,22 +9427,22 @@
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@@ -9450,22 +9450,22 @@
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@@ -9496,22 +9496,22 @@
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@@ -9519,22 +9519,22 @@
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@@ -9542,22 +9542,22 @@
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@@ -9565,22 +9565,22 @@
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@@ -9588,22 +9588,22 @@
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@@ -9611,22 +9611,22 @@
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@@ -9634,22 +9634,22 @@
    "group": "euclid_vis_lp",
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@@ -9657,22 +9657,22 @@
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@@ -9680,22 +9680,22 @@
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@@ -9703,22 +9703,22 @@
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@@ -9726,22 +9726,22 @@
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@@ -9749,22 +9749,22 @@
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@@ -9910,22 +9910,22 @@
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@@ -9933,22 +9933,22 @@
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@@ -10094,22 +10094,22 @@
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@@ -10117,22 +10117,22 @@
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@@ -10140,22 +10140,22 @@
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@@ -10163,22 +10163,22 @@
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@@ -10186,22 +10186,22 @@
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@@ -10209,22 +10209,22 @@
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@@ -10232,22 +10232,22 @@
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@@ -10255,22 +10255,22 @@
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@@ -10278,22 +10278,22 @@
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@@ -10301,22 +10301,22 @@
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@@ -10324,22 +10324,22 @@
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@@ -10347,22 +10347,22 @@
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@@ -10623,22 +10623,22 @@
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@@ -10646,22 +10646,22 @@
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@@ -10692,22 +10692,22 @@
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@@ -10761,22 +10761,22 @@
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@@ -10784,22 +10784,22 @@
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@@ -10807,22 +10807,22 @@
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@@ -10830,22 +10830,22 @@
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@@ -10899,22 +10899,22 @@
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@@ -10922,22 +10922,22 @@
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@@ -10991,22 +10991,22 @@
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@@ -11014,22 +11014,22 @@
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@@ -11037,22 +11037,22 @@
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@@ -11060,22 +11060,22 @@
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@@ -11083,22 +11083,22 @@
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@@ -11106,22 +11106,22 @@
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@@ -11129,22 +11129,22 @@
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@@ -11152,22 +11152,22 @@
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@@ -11175,22 +11175,22 @@
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@@ -11198,22 +11198,22 @@
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@@ -11221,22 +11221,22 @@
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@@ -11244,22 +11244,22 @@
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@@ -11267,22 +11267,22 @@
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   {
@@ -11290,22 +11290,22 @@
    "group": "slam_spread_hst",
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   {
@@ -11313,22 +11313,22 @@
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   {
@@ -11336,22 +11336,22 @@
    "group": "slam_spread_hst",
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   {
@@ -11359,22 +11359,22 @@
    "group": "slam_spread_hst",
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   {
@@ -11382,22 +11382,22 @@
    "group": "slam_spread_hst",
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   {
@@ -11405,22 +11405,22 @@
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   {
@@ -11428,22 +11428,22 @@
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   {
@@ -11451,22 +11451,22 @@
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   {
@@ -11474,22 +11474,22 @@
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   {
@@ -11497,22 +11497,22 @@
    "group": "euclid_vis_lp",
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   {
@@ -11520,22 +11520,22 @@
    "group": "slam_fixture_571",
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@@ -11589,22 +11589,22 @@
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@@ -11612,22 +11612,22 @@
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@@ -11635,22 +11635,22 @@
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@@ -11658,22 +11658,22 @@
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@@ -11681,22 +11681,22 @@
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@@ -11704,22 +11704,22 @@
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@@ -11727,22 +11727,22 @@
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@@ -11750,22 +11750,22 @@
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@@ -11773,22 +11773,22 @@
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@@ -11796,22 +11796,22 @@
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@@ -11819,22 +11819,22 @@
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@@ -11888,22 +11888,22 @@
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@@ -12003,22 +12003,22 @@
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@@ -12026,22 +12026,22 @@
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@@ -12049,22 +12049,22 @@
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@@ -12118,22 +12118,22 @@
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@@ -12141,22 +12141,22 @@
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@@ -12164,22 +12164,22 @@
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@@ -12187,22 +12187,22 @@
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@@ -12210,22 +12210,22 @@
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@@ -12233,22 +12233,22 @@
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@@ -12256,22 +12256,22 @@
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@@ -12279,22 +12279,22 @@
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@@ -12302,22 +12302,22 @@
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@@ -12325,22 +12325,22 @@
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@@ -12348,22 +12348,22 @@
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@@ -12371,22 +12371,22 @@
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@@ -12394,22 +12394,22 @@
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@@ -12440,22 +12440,22 @@
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@@ -12463,22 +12463,22 @@
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@@ -12486,22 +12486,22 @@
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@@ -12509,22 +12509,22 @@
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@@ -12532,22 +12532,22 @@
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@@ -12555,22 +12555,22 @@
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@@ -12578,22 +12578,22 @@
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@@ -12601,22 +12601,22 @@
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@@ -12624,22 +12624,22 @@
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@@ -12647,22 +12647,22 @@
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@@ -12670,22 +12670,22 @@
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@@ -12693,22 +12693,22 @@
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@@ -12739,22 +12739,22 @@
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@@ -12762,22 +12762,22 @@
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@@ -12785,22 +12785,22 @@
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@@ -12808,22 +12808,22 @@
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@@ -12831,22 +12831,22 @@
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@@ -12854,22 +12854,22 @@
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@@ -12877,22 +12877,22 @@
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@@ -12900,22 +12900,22 @@
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@@ -12923,22 +12923,22 @@
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@@ -12946,22 +12946,22 @@
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@@ -12969,22 +12969,22 @@
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@@ -12992,22 +12992,22 @@
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@@ -13015,22 +13015,22 @@
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@@ -13038,22 +13038,22 @@
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@@ -13061,22 +13061,22 @@
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@@ -13084,22 +13084,22 @@
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@@ -13107,22 +13107,22 @@
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@@ -13130,22 +13130,22 @@
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@@ -13153,22 +13153,22 @@
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@@ -13176,22 +13176,22 @@
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@@ -13199,22 +13199,22 @@
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@@ -13222,22 +13222,22 @@
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@@ -13245,22 +13245,22 @@
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@@ -13291,22 +13291,22 @@
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@@ -13314,22 +13314,22 @@
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@@ -13337,22 +13337,22 @@
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   {
@@ -13360,22 +13360,22 @@
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@@ -13383,22 +13383,22 @@
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@@ -13406,22 +13406,22 @@
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@@ -13429,22 +13429,22 @@
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@@ -13452,22 +13452,22 @@
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@@ -13475,22 +13475,22 @@
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@@ -13498,22 +13498,22 @@
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   {
@@ -13521,22 +13521,22 @@
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@@ -13544,22 +13544,22 @@
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   {
@@ -13567,22 +13567,22 @@
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@@ -13613,22 +13613,22 @@
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@@ -13636,22 +13636,22 @@
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@@ -13659,22 +13659,22 @@
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@@ -13682,22 +13682,22 @@
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@@ -13705,22 +13705,22 @@
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@@ -13728,22 +13728,22 @@
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@@ -13751,22 +13751,22 @@
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@@ -13774,22 +13774,22 @@
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@@ -13797,22 +13797,22 @@
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@@ -13820,22 +13820,22 @@
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@@ -13866,22 +13866,22 @@
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@@ -13889,22 +13889,22 @@
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@@ -13912,22 +13912,22 @@
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@@ -13935,22 +13935,22 @@
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@@ -13958,22 +13958,22 @@
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@@ -13981,22 +13981,22 @@
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@@ -14004,22 +14004,22 @@
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@@ -14027,22 +14027,22 @@
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@@ -14050,22 +14050,22 @@
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@@ -14073,22 +14073,22 @@
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@@ -14096,22 +14096,22 @@
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   {
@@ -14119,22 +14119,22 @@
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@@ -14142,22 +14142,22 @@
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@@ -14165,22 +14165,22 @@
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@@ -14211,22 +14211,22 @@
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@@ -14234,22 +14234,22 @@
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@@ -14257,22 +14257,22 @@
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@@ -14280,22 +14280,22 @@
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@@ -14303,22 +14303,22 @@
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@@ -14326,22 +14326,22 @@
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@@ -14349,22 +14349,22 @@
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@@ -14372,22 +14372,22 @@
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@@ -14395,22 +14395,22 @@
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@@ -14418,22 +14418,22 @@
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@@ -14441,22 +14441,22 @@
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@@ -14464,22 +14464,22 @@
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@@ -14487,22 +14487,22 @@
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@@ -14510,22 +14510,22 @@
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   {
@@ -14533,22 +14533,22 @@
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@@ -14556,22 +14556,22 @@
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@@ -14579,22 +14579,22 @@
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@@ -14602,22 +14602,22 @@
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@@ -14625,22 +14625,22 @@
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@@ -14648,22 +14648,22 @@
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   {
@@ -14671,22 +14671,22 @@
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   {
@@ -14694,22 +14694,22 @@
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   {
@@ -14717,22 +14717,22 @@
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   {
@@ -14740,22 +14740,22 @@
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@@ -14763,22 +14763,22 @@
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@@ -14786,22 +14786,22 @@
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@@ -14809,22 +14809,22 @@
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@@ -14832,22 +14832,22 @@
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@@ -14855,22 +14855,22 @@
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@@ -14878,22 +14878,22 @@
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@@ -14901,22 +14901,22 @@
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@@ -14924,22 +14924,22 @@
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@@ -14947,22 +14947,22 @@
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@@ -14970,22 +14970,22 @@
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@@ -14993,22 +14993,22 @@
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@@ -15016,22 +15016,22 @@
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@@ -15039,22 +15039,22 @@
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-   "wall_ms": 1.2325000006967457,
+   "wall_ms": 1.9993999958387576,
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   {
@@ -15062,22 +15062,22 @@
    "group": "slam_spread_hst",
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-   "kkt_residual_scaled": 2.312076424696311e-17,
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    "iterations": 24,
    "stats": {},
-   "wall_ms": 1.2944999998580897,
+   "wall_ms": 2.0801999999093823,
    "cap": 200
   },
   {
@@ -15085,22 +15085,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
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-   "complementarity": 8.006787040000093e-16,
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    "converged": true,
    "iterations": 14,
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-   "wall_ms": 0.9642999993957346,
+   "wall_ms": 1.7600000064703636,
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   {
@@ -15108,22 +15108,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
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-   "objective_gap": 5.386115848920224e-14,
+   "amp_rel_l2": 3.666206297252477e-07,
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+   "flux_rel_source": 3.575049424913267e-06,
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-   "dual_violation": 0.0,
-   "complementarity": 1.7137453030867862e-16,
-   "kkt_residual_scaled": 1.7137453030867862e-16,
+   "dual_violation": 3.034245139081921e-17,
+   "complementarity": 3.628638558496287e-19,
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    "converged": true,
    "iterations": 15,
    "stats": {},
-   "wall_ms": 0.9417000001121778,
+   "wall_ms": 1.8735999983618967,
    "cap": 200
   },
   {
@@ -15131,22 +15131,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 5.564368943592576e-11,
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-   "flux_rel_source": 1.799505178474084e-09,
-   "flux_inactive_rel": 1.9590658223738687e-11,
+   "amp_rel_l2": 5.669090160181447e-13,
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+   "flux_rel_all": 2.3747214518127007e-13,
+   "flux_rel_source": 1.7914920662171576e-11,
+   "flux_inactive_rel": 1.959065822373936e-13,
    "active_set_mismatch": 0,
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    "primal_violation": 0.0,
-   "dual_violation": 5.92775319866286e-20,
-   "complementarity": 1.3963640602416758e-18,
-   "kkt_residual_scaled": 1.3963640602416758e-18,
+   "dual_violation": 1.2140038550861537e-16,
+   "complementarity": 1.5127020594045762e-19,
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    "converged": true,
    "iterations": 12,
    "stats": {},
-   "wall_ms": 0.8199000003514811,
+   "wall_ms": 1.3293000010889955,
    "cap": 200
   },
   {
@@ -15154,22 +15154,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 3.368391041692487e-05,
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+   "amp_rel_l2": 7.132511276192048e-07,
+   "amp_rel_max": 1.1810232388526993e-11,
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+   "flux_rel_all": 3.3471314251423373e-07,
+   "flux_rel_source": 3.163098965165106e-05,
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    "active_set_mismatch": 2,
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+   "objective_gap": 5.429945901793703e-16,
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-   "dual_violation": 0.0,
-   "complementarity": 6.806332316749537e-16,
-   "kkt_residual_scaled": 6.806332316749537e-16,
+   "dual_violation": 7.584513190875085e-18,
+   "complementarity": 5.612204626532637e-19,
+   "kkt_residual_scaled": 7.584513190875085e-18,
    "converged": true,
    "iterations": 14,
    "stats": {},
-   "wall_ms": 0.9121000002778601,
+   "wall_ms": 1.603899996553082,
    "cap": 200
   },
   {
@@ -15177,22 +15177,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
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-   "objective_gap": 1.195673347803099e-13,
+   "amp_rel_l2": 5.036346277276895e-07,
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+   "amp_rel_max_sig": 0.2186910396320344,
+   "flux_rel_all": 2.7684073403333974e-07,
+   "flux_rel_source": 1.4833666603310154e-05,
+   "flux_inactive_rel": 2.3310027472475101e-07,
+   "active_set_mismatch": 0,
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    "primal_violation": 0.0,
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-   "complementarity": 4.020840136784206e-16,
-   "kkt_residual_scaled": 4.020840136784206e-16,
+   "dual_violation": 1.2134919065268297e-16,
+   "complementarity": 4.401445137909511e-19,
+   "kkt_residual_scaled": 1.2134919065268297e-16,
    "converged": true,
    "iterations": 15,
    "stats": {},
-   "wall_ms": 0.921199998629163,
+   "wall_ms": 1.446199996280484,
    "cap": 200
   },
   {
@@ -15200,22 +15200,22 @@
    "group": "slam_spread_hst",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 2.3149545006860793e-05,
-   "amp_rel_max": 5.4404178422189836e-09,
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-   "flux_rel_all": 1.5552152343095705e-05,
-   "flux_rel_source": 0.001117883257093271,
-   "flux_inactive_rel": 1.555217035442762e-05,
-   "active_set_mismatch": 4,
-   "objective_gap": 1.4582157121914216e-13,
+   "amp_rel_l2": 4.5850711978888697e-07,
+   "amp_rel_max": 6.098560194584461e-12,
+   "amp_rel_max_sig": 6.098560194584461e-12,
+   "flux_rel_all": 2.701870390519458e-07,
+   "flux_rel_source": 1.9420943625289446e-05,
+   "flux_inactive_rel": 2.7018705103514484e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": 8.176910535652831e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 2.884603557794601e-16,
-   "kkt_residual_scaled": 2.884603557794601e-16,
+   "dual_violation": 2.427672221859022e-16,
+   "complementarity": 2.8473254518136575e-19,
+   "kkt_residual_scaled": 2.427672221859022e-16,
    "converged": true,
    "iterations": 16,
    "stats": {},
-   "wall_ms": 0.9812000007514143,
+   "wall_ms": 1.767399997333996,
    "cap": 200
   },
   {
@@ -15223,22 +15223,22 @@
    "group": "euclid_vis_lp",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 0.04336774574740038,
-   "amp_rel_max": 18482448391.3038,
-   "amp_rel_max_sig": 1.042293729386814e-08,
-   "flux_rel_all": 0.11522530752020606,
-   "flux_rel_source": 16.355202018136207,
-   "flux_inactive_rel": 0.11522530892329123,
-   "active_set_mismatch": 26,
-   "objective_gap": 2.6084263568677235e-12,
+   "amp_rel_l2": 0.00013209142455026892,
+   "amp_rel_max": 59293868.893226236,
+   "amp_rel_max_sig": 5.238376163843649e-13,
+   "flux_rel_all": 0.0003309317052181464,
+   "flux_rel_source": 0.049615932161212996,
+   "flux_inactive_rel": 0.0003309329699063631,
+   "active_set_mismatch": 23,
+   "objective_gap": 1.2940548478780193e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 2.6623732310776797e-14,
-   "kkt_residual_scaled": 2.6623732310776797e-14,
+   "dual_violation": 5.481651318885829e-17,
+   "complementarity": 1.8363781640294188e-17,
+   "kkt_residual_scaled": 5.481651318885829e-17,
    "converged": true,
    "iterations": 24,
    "stats": {},
-   "wall_ms": 1.2669999996433035,
+   "wall_ms": 2.5286000018240884,
    "cap": 200
   }
  ]
diff --git a/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.png b/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.png
index 25747a861..2598552ee 100644
Binary files a/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.png and b/results/lens/solver/early_stopping_summary_all_v2026.8.17.1.png differ
diff --git a/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.json b/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.json
index 7f99a1fba..84e4550e0 100644
--- a/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.json
+++ b/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.json
@@ -12,7 +12,7 @@
   "cache_fresh": true,
   "provenance": {
    "provenance_schema": 1,
-   "captured_at": "2026-09-30T10:12:45Z",
+   "captured_at": "2026-09-30T16:56:05Z",
    "host": "DESKTOP-H143S82",
    "slurm": {
     "job_id": null,
@@ -20,23 +20,23 @@
     "array_task_id": null
    },
    "loadavg_at_import": [
-    0.572,
-    0.595,
-    1.018
+    5.954,
+    2.549,
+    1.414
    ],
    "loadavg_at_write": [
-    0.572,
-    0.595,
-    1.018
+    5.954,
+    2.549,
+    1.414
    ],
-   "profiling_revision": "2da9c29831325d91a737cf5316e19b5cc20705f7",
+   "profiling_revision": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
    "library_revisions": {
-    "autolens_profiling": "2da9c29831325d91a737cf5316e19b5cc20705f7",
+    "autolens_profiling": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
     "PyAutoNerves": "1ec1c829acec0ce85a7eddf791eb66574d507884",
-    "PyAutoFit": "3fa8cf7f26dd51c9d00199f37176cc68ffb48356",
-    "PyAutoArray": "d4298445e8fc2c93be4b9312b480808b05047805",
-    "PyAutoGalaxy": "4900e6809101aa9e40ce40dc1bfa0c89874939ee",
-    "PyAutoLens": "092897e48adaed46c00574032bbdb59d340d57e0"
+    "PyAutoFit": "b13169e2ced2c44ed2b4a43f3a6ef1c92df989d4",
+    "PyAutoArray": "7a89e19a09760a0daf22f40e8b111b5388767f73",
+    "PyAutoGalaxy": "4c834cedee64a6fe530c7ac66d77de21cca346e8",
+    "PyAutoLens": "efd13c4cdfb7ab0d4a37549468998e06e31f27d7"
    },
    "library_versions": {
     "PyAutoNerves": "2026.8.17.1",
@@ -103,7 +103,7 @@
    100,
    200
   ],
-  "description": "Released default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library returns y = x / D and the reconstruction y * D, reproduced exactly. Extras: the backward-pass polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
+  "description": "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw (Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and interior) \u2014 reconstruction y * D, reproduced exactly. converged / iterations are the raw forward solve's (the polish is not counted). Extras: the polish / relaxed-KKT status from raw_forward_backward_status (not timed).",
   "library_function": "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward"
  },
  "aggregates": {
@@ -117,11 +117,14 @@
    "worst_amp_rel_l2": 0.2051149438288252,
    "worst_flux_rel_all": 0.09087205817506977,
    "worst_flux_rel_source": 25.575411418239035,
+   "worst_flux_inactive_rel": 0.15077080569657159,
+   "median_flux_inactive_rel": 0.07931942866626576,
+   "worst_active_set_mismatch": 29,
    "worst_objective_gap": 0.00016886466499293917,
    "worst_kkt_residual_scaled": 0.0003021080723874789,
    "median_iterations": 8.0,
    "max_iterations": 8,
-   "median_wall_ms": 0.7255499995153514
+   "median_wall_ms": 1.95144999815966
   },
   "12": {
    "n_systems": 8,
@@ -133,107 +136,128 @@
    "worst_amp_rel_l2": 0.04624488706454652,
    "worst_flux_rel_all": 0.041112230540967694,
    "worst_flux_rel_source": 6.707092656296992,
+   "worst_flux_inactive_rel": 0.0339465238895694,
+   "median_flux_inactive_rel": 0.007621139697826906,
+   "worst_active_set_mismatch": 28,
    "worst_objective_gap": 1.1806630875021006e-06,
    "worst_kkt_residual_scaled": 4.139623068525553e-07,
    "median_iterations": 12.0,
    "max_iterations": 12,
-   "median_wall_ms": 0.9398000001965556
+   "median_wall_ms": 2.4086500015982892
   },
   "16": {
    "n_systems": 8,
    "n_unconverged": 7,
    "n_nonfinite": 0,
-   "worst_amp_rel_max": 58660218.48207359,
-   "median_amp_rel_max": 2671.7380681374866,
-   "worst_amp_rel_max_sig": 24.590887023936663,
-   "worst_amp_rel_l2": 0.0009042034336203298,
-   "worst_flux_rel_all": 0.0007727070168906368,
-   "worst_flux_rel_source": 0.12482668226448401,
-   "worst_objective_gap": 6.130983226675388e-11,
-   "worst_kkt_residual_scaled": 6.114148503580637e-14,
+   "worst_amp_rel_max": 69076.58656671505,
+   "median_amp_rel_max": 1.042150949480081,
+   "worst_amp_rel_max_sig": 4.953172555585091e-06,
+   "worst_amp_rel_l2": 4.2883367385144805e-07,
+   "worst_flux_rel_all": 3.05210446906404e-07,
+   "worst_flux_rel_source": 4.93049260640389e-05,
+   "worst_flux_inactive_rel": 3.442335432581026e-07,
+   "median_flux_inactive_rel": 2.2074835459363753e-07,
+   "worst_active_set_mismatch": 0,
+   "worst_objective_gap": 3.604723734631914e-16,
+   "worst_kkt_residual_scaled": 1.0790253271454604e-16,
    "median_iterations": 16.0,
    "max_iterations": 16,
-   "median_wall_ms": 1.045949999934237
+   "median_wall_ms": 2.489999998942949
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   "24": {
    "n_systems": 8,
    "n_unconverged": 0,
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-   "worst_amp_rel_max": 22381504.841336958,
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@@ -935,20 +1025,22 @@
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@@ -956,20 +1048,22 @@
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@@ -977,20 +1071,22 @@
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@@ -998,20 +1094,22 @@
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@@ -1019,20 +1117,22 @@
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@@ -1061,20 +1163,22 @@
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@@ -1082,20 +1186,22 @@
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@@ -1103,20 +1209,22 @@
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@@ -1187,20 +1301,22 @@
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@@ -1208,20 +1324,22 @@
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@@ -1229,20 +1347,22 @@
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   {
@@ -1250,20 +1370,22 @@
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   {
@@ -1271,20 +1393,22 @@
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@@ -1292,20 +1416,22 @@
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@@ -1313,20 +1439,22 @@
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@@ -1334,20 +1462,22 @@
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@@ -1355,20 +1485,22 @@
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@@ -1376,20 +1508,22 @@
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   {
@@ -1397,20 +1531,22 @@
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@@ -1418,20 +1554,22 @@
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@@ -1439,20 +1577,22 @@
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+   "objective_gap": -1.201574578210638e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 9.154706627126224e-17,
-   "kkt_residual_scaled": 9.154706627126224e-17,
+   "dual_violation": 1.0787237921726043e-16,
+   "complementarity": 1.2533554191999002e-19,
+   "kkt_residual_scaled": 1.0787237921726043e-16,
    "converged": true,
    "iterations": 17,
    "stats": {},
-   "wall_ms": 1.2614999996003462,
+   "wall_ms": 2.614499993796926,
    "cap": 200
   },
   {
@@ -1460,20 +1600,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 7.381851338721688e-05,
-   "amp_rel_max": 22381504.841336958,
-   "amp_rel_max_sig": 0.009420217853137713,
-   "flux_rel_all": 5.0247463821039846e-05,
-   "flux_rel_source": 0.0012204464571833411,
-   "objective_gap": 3.43838655903869e-13,
+   "amp_rel_l2": 2.2775419842479827e-07,
+   "amp_rel_max": 69076.58656671374,
+   "amp_rel_max_sig": 2.055680295600251e-09,
+   "flux_rel_all": 1.5282075045481795e-07,
+   "flux_rel_source": 3.711820098031724e-06,
+   "flux_inactive_rel": 1.5282132292651462e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": -3.601661898434382e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 5.244884306587396e-16,
-   "kkt_residual_scaled": 5.244884306587396e-16,
+   "dual_violation": 1.0789573829364653e-16,
+   "complementarity": 1.8306947140614647e-19,
+   "kkt_residual_scaled": 1.0789573829364653e-16,
    "converged": true,
    "iterations": 17,
    "stats": {},
-   "wall_ms": 1.1849999991682125,
+   "wall_ms": 2.335100005439017,
    "cap": 200
   },
   {
@@ -1481,20 +1623,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 6.715435355245617e-05,
-   "amp_rel_max": 3.2581049384324714e-08,
-   "amp_rel_max_sig": 3.2581049384324714e-08,
-   "flux_rel_all": 5.359322145958006e-05,
-   "flux_rel_source": 0.004043641903097979,
-   "objective_gap": 4.1437346653877226e-13,
+   "amp_rel_l2": 1.8875290584632482e-07,
+   "amp_rel_max": 4.557884252862277e-12,
+   "amp_rel_max_sig": 4.557884252862277e-12,
+   "flux_rel_all": 1.3107610796591497e-07,
+   "flux_rel_source": 9.889779909310281e-06,
+   "flux_inactive_rel": 1.310761047694486e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": -2.4021650234131725e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 3.9580460912365565e-16,
-   "kkt_residual_scaled": 3.9580460912365565e-16,
+   "dual_violation": 1.0788261495545102e-16,
+   "complementarity": 1.4305435681386126e-19,
+   "kkt_residual_scaled": 1.0788261495545102e-16,
    "converged": true,
    "iterations": 16,
    "stats": {},
-   "wall_ms": 1.1326000003464287,
+   "wall_ms": 3.358699999807868,
    "cap": 200
   },
   {
@@ -1502,20 +1646,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 4.085436529288806e-05,
-   "amp_rel_max": 108.37834482377158,
-   "amp_rel_max_sig": 2.4351349979027067e-08,
-   "flux_rel_all": 3.341439746350453e-05,
-   "flux_rel_source": 0.0006681575701525225,
-   "objective_gap": 2.283392030679173e-13,
+   "amp_rel_l2": 3.004233471122932e-07,
+   "amp_rel_max": 0.4740692198256952,
+   "amp_rel_max_sig": 1.0744191788581896e-11,
+   "flux_rel_all": 2.396466058206484e-07,
+   "flux_rel_source": 4.7919971426203754e-06,
+   "flux_inactive_rel": 3.164592130691841e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": -2.413733647652402e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 1.719432280409046e-16,
-   "kkt_residual_scaled": 1.719432280409046e-16,
+   "dual_violation": 1.0788880343590817e-16,
+   "complementarity": 1.2539636609622228e-19,
+   "kkt_residual_scaled": 1.0788880343590817e-16,
    "converged": true,
    "iterations": 18,
    "stats": {},
-   "wall_ms": 1.2879999994765967,
+   "wall_ms": 3.4852000026148744,
    "cap": 200
   },
   {
@@ -1523,20 +1669,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 4.9661133676208636e-05,
-   "amp_rel_max": 255.07871702427545,
-   "amp_rel_max_sig": 5.3986873502424485e-06,
-   "flux_rel_all": 3.9876411995863e-05,
-   "flux_rel_source": 0.006441797500967694,
-   "objective_gap": 1.8185114616763172e-13,
+   "amp_rel_l2": 3.8208815110283914e-07,
+   "amp_rel_max": 1.610232679134452,
+   "amp_rel_max_sig": 2.8977807847914245e-10,
+   "flux_rel_all": 3.052104435920311e-07,
+   "flux_rel_source": 4.930492604392857e-05,
+   "flux_inactive_rel": 3.442335432581014e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": -1.209116663348615e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 1.8897889123238535e-16,
-   "kkt_residual_scaled": 1.8897889123238535e-16,
+   "dual_violation": 2.1579051306233102e-16,
+   "complementarity": 2.765810689295915e-19,
+   "kkt_residual_scaled": 2.1579051306233102e-16,
    "converged": true,
    "iterations": 19,
    "stats": {},
-   "wall_ms": 1.335799999651499,
+   "wall_ms": 2.70019999879878,
    "cap": 200
   },
   {
@@ -1544,20 +1692,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 5.930010291398057e-05,
-   "amp_rel_max": 3.177475986876165,
-   "amp_rel_max_sig": 3.177475986876165,
-   "flux_rel_all": 4.558583595760823e-05,
-   "flux_rel_source": 0.0009227184154652847,
-   "objective_gap": 2.577299162058004e-13,
+   "amp_rel_l2": 4.2883367385145313e-07,
+   "amp_rel_max": 4.953172552819985e-06,
+   "amp_rel_max_sig": 4.953172552819985e-06,
+   "flux_rel_all": 2.8599572906372685e-07,
+   "flux_rel_source": 5.7889369905233566e-06,
+   "flux_inactive_rel": 2.8597976282937655e-07,
+   "active_set_mismatch": 0,
+   "objective_gap": 1.2026594316649576e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 2.881895498788576e-16,
-   "kkt_residual_scaled": 2.881895498788576e-16,
+   "dual_violation": 1.0790253271454604e-16,
+   "complementarity": 3.51790168899895e-19,
+   "kkt_residual_scaled": 1.0790253271454604e-16,
    "converged": true,
    "iterations": 18,
    "stats": {},
-   "wall_ms": 1.2141000006522518,
+   "wall_ms": 2.395899995462969,
    "cap": 200
   },
   {
@@ -1565,20 +1715,22 @@
    "group": "slam_fixture_571",
    "candidate": "pdip_raw_cap_200",
    "finite": true,
-   "amp_rel_l2": 4.0775246618704125e-05,
-   "amp_rel_max": 2432.3029524710323,
-   "amp_rel_max_sig": 0.001808660401156372,
-   "flux_rel_all": 1.6115758404294993e-05,
-   "flux_rel_source": 0.0009496854684437486,
-   "objective_gap": 8.304976389869099e-14,
+   "amp_rel_l2": 1.1724529736525167e-07,
+   "amp_rel_max": 6.994356604174572,
+   "amp_rel_max_sig": 3.3274909649576264e-10,
+   "flux_rel_all": 4.580193526048058e-08,
+   "flux_rel_source": 2.6990621177590934e-06,
+   "flux_inactive_rel": 5.235036587931727e-08,
+   "active_set_mismatch": 0,
+   "objective_gap": 2.3968185829348047e-16,
    "primal_violation": 0.0,
-   "dual_violation": 0.0,
-   "complementarity": 4.622529695151958e-16,
-   "kkt_residual_scaled": 4.622529695151958e-16,
+   "dual_violation": 1.078784486670063e-16,
+   "complementarity": 2.865520610543578e-19,
+   "kkt_residual_scaled": 1.078784486670063e-16,
    "converged": true,
    "iterations": 18,
    "stats": {},
-   "wall_ms": 1.2062999994668644,
+   "wall_ms": 2.5103000007220544,
    "cap": 200
   }
  ]
diff --git a/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.png b/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.png
index cac469ae0..4c24a3deb 100644
Binary files a/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.png and b/results/lens/solver/early_stopping_summary_slam_fixture_571_v2026.8.17.1.png differ
diff --git a/results/lens/solver/euclid_latent_by_candidate_v2026.8.17.1.json b/results/lens/solver/euclid_latent_by_candidate_v2026.8.17.1.json
index c7e057362..a37c4a389 100644
--- a/results/lens/solver/euclid_latent_by_candidate_v2026.8.17.1.json
+++ b/results/lens/solver/euclid_latent_by_candidate_v2026.8.17.1.json
@@ -12,7 +12,7 @@
   "cache_fresh": true,
   "provenance": {
    "provenance_schema": 1,
-   "captured_at": "2026-09-30T10:37:56Z",
+   "captured_at": "2026-09-30T16:56:40Z",
    "host": "DESKTOP-H143S82",
    "slurm": {
     "job_id": null,
@@ -20,23 +20,23 @@
     "array_task_id": null
    },
    "loadavg_at_import": [
-    2.117,
-    1.92,
-    1.733
+    6.957,
+    3.171,
+    1.665
    ],
    "loadavg_at_write": [
-    2.117,
-    1.92,
-    1.733
+    6.957,
+    3.171,
+    1.665
    ],
-   "profiling_revision": "8adb629d658a143498ae2555ab1601dc72dca032",
+   "profiling_revision": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
    "library_revisions": {
-    "autolens_profiling": "8adb629d658a143498ae2555ab1601dc72dca032",
+    "autolens_profiling": "44381ec8d25d2f877f9853224bb8dc75b426ecff",
     "PyAutoNerves": "1ec1c829acec0ce85a7eddf791eb66574d507884",
-    "PyAutoFit": "3fa8cf7f26dd51c9d00199f37176cc68ffb48356",
-    "PyAutoArray": "d4298445e8fc2c93be4b9312b480808b05047805",
-    "PyAutoGalaxy": "4900e6809101aa9e40ce40dc1bfa0c89874939ee",
-    "PyAutoLens": "092897e48adaed46c00574032bbdb59d340d57e0"
+    "PyAutoFit": "b13169e2ced2c44ed2b4a43f3a6ef1c92df989d4",
+    "PyAutoArray": "7a89e19a09760a0daf22f40e8b111b5388767f73",
+    "PyAutoGalaxy": "4c834cedee64a6fe530c7ac66d77de21cca346e8",
+    "PyAutoLens": "efd13c4cdfb7ab0d4a37549468998e06e31f27d7"
    },
    "library_versions": {
     "PyAutoNerves": "2026.8.17.1",
@@ -61,7 +61,8 @@
  "latent_key": "total_source_flux",
  "latent_reference": {
   "eager_numpy": 3.3198794874035915,
-  "jit_released": 3.511093374207152
+  "jit_released": 3.511093374207152,
+  "jit_library_main": 3.320127603567922
  },
  "method": "reconstruction injected through inversion_util.reconstruction_positive_only_from into the eager NumPy fit; total_source_flux from LatentEuclid._source_flux_latents_on_uniform_grid",
  "euclid": {
@@ -78,10 +79,11 @@
    "stub_calls": 1
   },
   "pdip_raw_to_jit": {
-   "value": 3.5110933039799486,
-   "expected": 3.511093374207152,
-   "rel": -2.0001519789772115e-08,
-   "stub_calls": 1
+   "value": 3.320127537168044,
+   "expected": 3.320127603567922,
+   "rel": -1.999919459993099e-08,
+   "stub_calls": 1,
+   "expected_source": "jitted LatentEuclid.variables on PyAutoArray main 7a89e19a0 (post-#595), euclid pipeline 26e4385b, 2026-09-30; replaces the pre-#595 released jit 3.511093374207152"
   },
   "rtol": 1e-06,
   "passed": true
@@ -108,9 +110,9 @@
   {
    "candidate": "pdip_raw",
    "posthoc": false,
-   "total_source_flux": 3.5110933039799486,
-   "rel_vs_eager": 0.0575966137632006,
-   "passes_test_rtol": false,
+   "total_source_flux": 3.320127537168044,
+   "rel_vs_eager": 7.471649660589918e-05,
+   "passes_test_rtol": true,
    "converged": true,
    "iterations": 24
   },
diff --git a/results/notes/linear_solver_accuracy_2026_09.md b/results/notes/linear_solver_accuracy_2026_09.md
index 45557f00c..5e1a697bd 100644
--- a/results/notes/linear_solver_accuracy_2026_09.md
+++ b/results/notes/linear_solver_accuracy_2026_09.md
@@ -1,4 +1,4 @@
-# Linear-solver accuracy study — ledger (phase 1, 2026-09)
+# Linear-solver accuracy study — ledger (phases 1–2, 2026-09)
 
 Campaign page: `wiki/campaigns/linear_solver_accuracy.md`. Package: `scripts/lens/solver/`.
 
@@ -84,7 +84,10 @@ deterministic on this host; only wall times move.
 | `pdip_raw_polish` | 0/81 | 0.219 | 4.96e-2 | 8.2e-16 | 3.2e-16 | 23 | 30 | 1.72 |
 | `certified` | 48/81 | 119 | 7.5e-2 | 2.6e-4 | 1.8e-2 | 16 | 16 | 1.12 |
 
-(`pdip_raw_tol_1e-2` reproduces `pdip_raw` exactly through `solve_nnls` directly, as designed.)
+(`pdip_raw_tol_1e-2` reproduces `pdip_raw` exactly through `solve_nnls` directly, as designed.
+(phase 1; no longer true after #595 — see [Phase 2](#phase-2-2026-09-30--library-fix-shipped-pyautoarray595):
+`pdip_raw` is now polished, the `tol_1e-2` row is not, and the row now reproduces the *pre-fix*
+`pdip_raw`.))
 
 ## Admissibility (the rule as written)
 
@@ -219,7 +222,8 @@ captured JAX one.
 | `pdip_raw_tol_1e-5` (post-hoc) | 3.3215684 | +5.1e-4 | yes | yes | 28 |
 | `pdip_raw_tol_1e-6` (post-hoc) | 3.3198794 | -3.3e-8 | yes | no | 50 |
 
-(`pdip_raw_tol_1e-2` is identical to `pdip_raw`.) Which candidates would turn the euclid test
+(`pdip_raw_tol_1e-2` is identical to `pdip_raw`. (phase 1; no longer true after #595 — see Phase 2:
+it still gives 3.5110933, the pre-fix value.)) Which candidates would turn the euclid test
 green, taken on their own: the forward polish (+7.5e-5, 30 iterations), the tolerance at 1e-5
 (+5.1e-4, 28), and jaxnnls's tolerance with a cap above 50 (exact, 51). The intensity-sum proxy of
 criterion 3 is much stricter than the latent: the polish's source-flux proxy error is 4.96e-2 but
@@ -288,3 +292,112 @@ one the numbers support.
 - Corpus: [`corpus/manifest.json`](../lens/solver/corpus/manifest.json).
 - Package and how to re-run: [`scripts/lens/solver/README.md`](../../scripts/lens/solver/README.md).
 - Campaign page: [`wiki/campaigns/linear_solver_accuracy.md`](../../wiki/campaigns/linear_solver_accuracy.md).
+
+## Phase 2 (2026-09-30) — library fix shipped: PyAutoArray#595
+
+**What changed in the library.** PyAutoArray#573 had added a *backward-pass* polish to the raw
+mode: after the raw forward PDIP stop, at most `RAW_POLISH_MAX_ITER` (10) PDIP iterations on the
+Jacobi-scaled system `(Q_pc, q_pc)` at jaxnnls's tight tolerance, warm-started from the mapped
+iterate `(y, s/D, z·D)` and kept only if it converges to a finite, strictly interior point — but
+used that point only for the gradient. The forward value stayed the raw stop, which is the value
+phase 1 found blind to flux on reference-inactive columns. PyAutoArray#595 (issue #594; merged
+2026-09-30 as merge `7a89e19a0`: red regression `7e62fa4d`, fix `31b1c2d7`, test-tolerance
+follow-up `42c52358`) returns the polished iterate as the forward value too, via one shared
+`_raw_forward_polished` that the custom-vjp primal and the forward rule both call, so plain,
+jitted and differentiated calls return the same `y`. This is option 2 of "What phase 2 should
+implement" (the forward polish), not option 1 (a solution-based stop). `converged` and the
+returned iteration count are still the raw forward solve's; the polish is not counted.
+
+**Re-run.** All five cells were re-run on 2026-09-30 16:54–16:57 UTC against the merged library
+(PyAutoArray `7a89e19a09760a0daf22f40e8b111b5388767f73`, recorded in every JSON's
+`device.provenance.library_revisions`; PyAutoFit `b13169e2`, PyAutoGalaxy `4c834ced`, PyAutoLens
+`efd13c4c`, PyAutoNerves `1ec1c829`; profiling `44381ec`; same host, jax 0.10.2). Walls: `accuracy.py`
+16 s, `--groups slam_fixture_571` 12 s, `--posthoc` 42 s, `early_stopping.py` 25 s,
+`--groups slam_fixture_571` 19 s, `euclid_latent.py` 33 s. The numbers reproduce the verifier's
+run on the pre-merge branch head to every quoted digit (the only library commit in between,
+`42c52358`, is a test-file change). Every candidate other than `pdip_raw` (and the
+`pdip_raw_cap_*` sweep, which calls the same library entry) is unchanged except wall time.
+
+**`pdip_raw` before and after** (81 systems, CPU fp64):
+
+| | Unconverged | Median / max iters | Worst abs(flux_inactive_rel) | Median flux_inactive_rel | Worst abs(flux_rel_all) | Worst abs(flux_rel_source) | Worst objective gap | Worst amp_rel_max_sig | Worst KKT |
+|---|---|---|---|---|---|---|---|---|---|
+| pre-#595 (phase 1, `d4298445`) | 0/81 | 18 / 24 | 0.115 | 3.2e-5 | 0.115 | 16.4 | 2.6e-12 | 27.0 | 2.7e-14 |
+| post-#595 (`7a89e19a0`) | 0/81 (48/48 `slam48_hst`) | 18 / 24 † | 3.31e-4 | 1.7e-7 | 3.31e-4 | 4.96e-2 (euclid only) | 8.2e-16 | 0.219 | 3.2e-16 |
+
+† Forward iterations only. The polish adds 1–7 more (5 on 46/81 systems, 6 on 21) and is
+accepted on 81/81 (`backward_polish_converged` = 1 everywhere); counting them, as the phase-1
+`pdip_raw_polish` candidate does, gives median 23 / max 30. Every metric field of every
+`pdip_raw` row is **identical** to the phase-1 `pdip_raw_polish` row on the same system (81/81),
+so the library now ships exactly the candidate phase 1 measured. On the 8 `slam_fixture_571`
+systems the worst source-flux error is 4.9e-5 and the worst significant-column error 5.0e-6. The
+0.219 `amp_rel_max_sig` is `slam_spread_hst/noise_x3_v32`, the flat-direction system every
+accurate candidate shares (see the admissibility notes); its source-flux error is 1.5e-5.
+Median warm wall 1.16 ms (pre-fix 0.99–1.36 ms across runs; indicative only).
+
+**The pre-registered rule on the fixed `pdip_raw`.** Still **not admissible as written**, for the
+rule weaknesses phase 1 already recorded; the library shipped on the flux-metric and latent
+evidence, not on this rule (which is not re-based here).
+
+| Criterion | Verdict | Why |
+|---|---|---|
+| 1. converged 100 % | **pass** | 81/81, all 48 `slam48_hst` included |
+| 2. sig ≤ 1e-3 and abs(flux_rel_source) ≤ 1e-4 | FAIL on 2 systems | `euclid_vis_lp_k0` flux_rel_source 4.96e-2 (its sig is 5e-13); `slam_spread_hst/noise_x3_v32` sig 0.219 (the flat direction; flux 1.5e-5) |
+| 3. euclid abs(flux_rel_source) ≤ 1e-4 | FAIL | 4.96e-2 — but the euclid latent itself is +7.47e-5 (below). The intensity-sum proxy is far stricter than the witness it stands for: euclid's single active source column carries ~0.4 % of the reference flux, so a small spurious amplitude on inactive source columns is a large fraction of the *source* sum, while those compact Gaussians contribute only 7.5e-5 to `total_source_flux` |
+| 4. KKT ≤ 10x `pdip_jacobi` | FAIL 5/52 | all at the floating-point floor: residuals 1.1e-16 – 3.2e-16 against jacobi's 3.4e-18 – 2.7e-17 (worst 32x). Pre-fix 8/52, worst 971x |
+
+**Euclid latent by candidate** (`euclid_latent.py`, euclid pipeline `26e4385b`). The validation
+leg `pdip_raw -> jit` was re-based from the capture-time released jit 3.511093374 to the jitted
+`LatentEuclid.variables` on library main, 3.320127604 (measured 2026-09-30 through the euclid
+test's own code; `euclid_latent.py`'s `PDIP_RAW_JIT_EXPECTED`); the manifest keeps the old value as
+the capture record. **Validation passed:** `x_ref` -> eager rel -3.26e-8, `pdip_raw` -> main jit
+rel -2.0e-8 (the same -2e-8 eager-vs-JAX system offset as phase 1).
+
+| Candidate | total_source_flux | rel vs eager 3.3198795 | Within the test's 1e-3 | Converged | Iterations |
+|---|---|---|---|---|---|
+| `fnnls` | 3.3198794 | -3.26e-8 | yes | yes | 8 |
+| `pdip_raw` (post-#595) | 3.3201275 | +7.47e-5 | **yes** | yes | 24 (forward) |
+| `pdip_raw` (pre-#595, phase 1) | 3.5110933 | +5.76e-2 | no | yes | 24 |
+| `pdip_raw_polish` | 3.3201275 | +7.47e-5 | yes | yes | 30 |
+| `pdip_jacobi` | 3.3202250 | +1.04e-4 | yes | yes | 19 |
+| `pdip_raw_tol_1e-2` | 3.5110933 | +5.76e-2 | no | yes | 24 |
+| `pdip_raw_tol_jaxnnls` | 3.3198794 | -3.26e-8 | yes | no | 50 |
+| `certified` | 3.3198794 | -3.26e-8 | yes | yes | 8 |
+
+The euclid pipeline's `tests/test_compute_latent_variable.py` passes 19/19 on library main
+(`JIT_VS_EAGER_REL` 1e-3), measured by the phase-2 verifier; the jitted latent above is +7.47e-5.
+
+**Correction: `pdip_raw_tol_1e-2` no longer reproduces `pdip_raw`.** It calls `solve_nnls`
+directly at the released tolerance with no polish, so since #595 it reproduces the *pre-fix*
+`pdip_raw` (worst source-flux 16.4, euclid latent +5.76e-2) rather than the current one. The
+phase-1 sentences saying otherwise are annotated above; the candidate descriptions in
+`_solvers.py` and the package README say so.
+
+**Early stopping after the fix.** The cap sweep calls the library entry, so the polish now
+follows every capped solve. From cap 24 up every row equals the fixed `pdip_raw` above (worst
+`flux_inactive_rel` 3.31e-4, source-flux 4.96e-2). Below it the converged counts are unchanged
+(0 / 1 / 15 of 81 at caps 8 / 12 / 16) and the worst-case errors are unchanged; only the cap-16 median `flux_inactive_rel` moves,
+1.2e-4 -> 2.0e-7. Caps below 24 remain unsafe.
+
+**Artefacts were overwritten in place.** The cells stamp `al.__version__`, which is `2026.8.17.1`
+on a source checkout, so the re-run rewrote every `results/lens/solver/*_v2026.8.17.1.{json,png}`
+(both `all` and `slam_fixture_571` summaries, the post-hoc summary and the euclid latent JSON).
+That is deliberate: the artefacts are "current library main" by contract. The pre-fix versions
+are at profiling commit **`3ad68af`** (git history), and their numbers are in the phase-1 tables
+above.
+
+**What phase 3 inherits.**
+
+- The shipped forward value is the polished iterate: 0/81 unconverged, `flux_inactive_rel` ≤
+  3.31e-4, euclid latent +7.47e-5 (inside the pipeline's 1e-3 with ~13x headroom), at 1–7 extra
+  PDIP iterations that the reported count hides — a GPU/vmap campaign should time the call, not
+  read the counter.
+- Option 1 (a solution-based stop / active-set certificate) is still unbuilt; the polish's own
+  stop is jaxnnls's absolute KKT tolerance, which on euclid leaves 3.3e-4 on inactive columns.
+  Anything that needs better than that on euclid-like systems still needs option 1.
+- The pre-registered rule needs re-basing before it can admit anything accurate: criterion 3
+  should gate on the latent (or on `flux_inactive_rel`), not the intensity-sum proxy; criterion 4
+  needs an absolute floor; criterion 2's significance floor admits the `noise_x3_v32` flat
+  direction.
+- The euclid cell's validation expectation is pinned to the library-main jit value; a future
+  solver change must re-measure and re-base it the same way.
diff --git a/scripts/lens/solver/README.md b/scripts/lens/solver/README.md
index d63b9b69b..735ca12bf 100644
--- a/scripts/lens/solver/README.md
+++ b/scripts/lens/solver/README.md
@@ -55,8 +55,8 @@ its registry entry. All return the reconstruction in raw coordinates exactly as
 |-----------|------------|----------------------|
 | `fnnls` | **Reference.** NumPy fnnls with incremental Cholesky from the dense-sign start — the NumPy production path (memo off). Defines `x_ref`; its row is a self-check. | `autoarray.util.fnnls.fnnls_cholesky` |
 | `pdip_jacobi` | Jacobi-preconditioned PDIP at jaxnnls's own tolerance, cap 50 — the library `"jacobi"` mode (default with a Mapper; the MGE default before PyAutoArray#571). | `jax_nnls.solve_nnls_primal_with_status` |
-| `pdip_raw` | **Released default for MGE (linear-object-only) inversions**: forward PDIP on the raw system at `data_scaled_solver_tol(q)`, cap 50, returned as `y * D`. Extras record the backward-pass polish / relaxed-KKT status. | `jax_nnls.solve_nnls_primal_raw_forward` (+ `raw_forward_backward_status`) |
-| `pdip_raw_tol_1e-1` / `_1e-2` / `_1e-3` | Raw forward PDIP with the data-scaled tolerance's factor set to 1e-1 / 1e-2 / 1e-3 in place of `DATA_SCALED_TOL_FACTOR` (1e-2 = released). | `jax_nnls.solve_nnls` + `data_scaled_solver_tol` |
+| `pdip_raw` | **Library default for MGE (linear-object-only) inversions**: forward PDIP on the raw system at `data_scaled_solver_tol(q)`, cap 50, mapped to the Jacobi system and — since PyAutoArray#595 — polished there by the #573 rule before being returned as `y * D`. `converged` / `iterations` are the forward solve's (the 1–7 polish iterations are not counted). Extras record the polish / relaxed-KKT status. | `jax_nnls.solve_nnls_primal_raw_forward` (+ `raw_forward_backward_status`) |
+| `pdip_raw_tol_1e-1` / `_1e-2` / `_1e-3` | Raw forward PDIP with the data-scaled tolerance's factor set to 1e-1 / 1e-2 / 1e-3 in place of `DATA_SCALED_TOL_FACTOR` (1e-2 = released), no polish — so since PyAutoArray#595 the 1e-2 row is the *pre-#595* `pdip_raw`, not the current one. | `jax_nnls.solve_nnls` + `data_scaled_solver_tol` |
 | `pdip_raw_tol_jaxnnls` | Raw forward PDIP at jaxnnls's absolute tolerance `min(n·eps·5e3, 1e-2)` — unreachable on an unscaled system, so it runs to the cap. | `jax_nnls.solve_nnls(solver_tol=None)` |
 | `pdip_raw_polish` | Raw forward, then ≤ `RAW_BACKWARD_POLISH_MAX_ITER` tight PDIP iterations on the Jacobi system warm-started from the mapped iterate, kept iff converged, finite and `s, z > 0` — PyAutoArray#573's backward rule applied to the *forward* value. | `jax_nnls.solve_nnls` (the `_raw_forward_backward_point` rule) |
 | `pdip_raw_cap_{8,12,16,24,32,50,100,200}` | `pdip_raw` at that `max_iter` (the `early_stopping.py` sweep). | `jax_nnls.solve_nnls_primal_raw_forward` |
@@ -94,24 +94,24 @@ and the median warm wall-clock (`wall_ms`, 5 repeats after one compile call).
 <!-- BEGIN auto-table:solver-accuracy -->
 | Corpus | Candidate | Unconverged | Non-finite | Worst amp_rel_max | Worst amp_rel_max (sig) | Worst abs(flux_rel_source) | Worst abs(flux_inactive_rel) | Worst objective gap | Worst KKT | Median iters | Median wall ms | Version |
 |--------|-----------|-------------|------------|-------------------|-------------------------|----------------------------|------------------------------|---------------------|-----------|--------------|----------------|---------|
-| `all` | `fnnls` | 0/81 | 0 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 8.05e-08 | 0.00e+00 | 8.63e-16 | 9.0 | 1.632 | v2026.8.17.1 |
-| `all` | `pdip_jacobi` | 29/81 | 0 | 2.73e+68 | 1.03e+68 | 3.36e+70 | 4.50e+68 | 1.56e+134 | 8.25e+61 | 19.0 | 1.323 | v2026.8.17.1 |
-| `all` | `pdip_raw` | 0/81 | 0 | 6.23e+10 | 2.70e+01 | 1.64e+01 | 1.15e-01 | 2.61e-12 | 2.66e-14 | 18.0 | 1.262 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-1` | 0/81 | 0 | 4.28e+11 | 7.15e+01 | 4.30e+01 | 3.02e-01 | 1.75e-11 | 1.80e-13 | 16.0 | 1.287 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-2` | 0/81 | 0 | 6.23e+10 | 2.70e+01 | 1.64e+01 | 1.15e-01 | 2.61e-12 | 2.66e-14 | 18.0 | 1.329 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-3` | 0/81 | 0 | 2.38e+10 | 1.00e+01 | 6.15e+00 | 4.37e-02 | 3.87e-13 | 3.93e-15 | 19.0 | 1.410 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_jaxnnls` | 75/81 | 0 | 4.30e+04 | 2.19e-01 | 7.40e-05 | 6.24e-07 | 8.18e-16 | 2.43e-16 | 50.0 | 3.073 | v2026.8.17.1 |
-| `all` | `pdip_raw_polish` | 0/81 | 0 | 5.03e+08 | 2.19e-01 | 4.96e-02 | 3.31e-04 | 8.18e-16 | 3.24e-16 | 23.0 | 1.713 | v2026.8.17.1 |
-| `all` | `certified` | 48/81 | 0 | 1.19e+02 | 1.19e+02 | 7.47e-02 | 1.49e-02 | 2.57e-04 | 1.81e-02 | 16.0 | 1.147 | v2026.8.17.1 |
-| `slam_fixture_571` | `fnnls` | 0/8 | 0 | 0.00e+00 | 0.00e+00 | 0.00e+00 | — | 0.00e+00 | 3.24e-16 | 7.0 | 0.932 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_jacobi` | 7/8 | 0 | 2.73e+68 | 1.03e+68 | 3.36e+70 | — | 1.56e+134 | 8.25e+61 | 50.0 | 2.490 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw` | 0/8 | 0 | 2.24e+07 | 3.18e+00 | 6.44e-03 | — | 4.14e-13 | 5.24e-16 | 18.0 | 1.205 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw_tol_1e-1` | 0/8 | 0 | 5.87e+07 | 9.08e+00 | 1.69e-02 | — | 2.73e-12 | 3.60e-15 | 17.0 | 1.275 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw_tol_1e-2` | 0/8 | 0 | 2.24e+07 | 3.18e+00 | 6.44e-03 | — | 4.14e-13 | 5.24e-16 | 18.0 | 1.445 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw_tol_1e-3` | 0/8 | 0 | 3.26e+06 | 9.54e-01 | 2.46e-03 | — | 3.55e-14 | 4.20e-17 | 19.0 | 1.314 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw_tol_jaxnnls` | 8/8 | 0 | 2.09e-11 | 2.09e-11 | 3.05e-13 | — | 6.01e-16 | 1.08e-16 | 50.0 | 2.593 | v2026.8.17.1 |
-| `slam_fixture_571` | `pdip_raw_polish` | 0/8 | 0 | 6.91e+04 | 4.95e-06 | 4.93e-05 | — | 3.60e-16 | 2.16e-16 | 23.0 | 1.376 | v2026.8.17.1 |
-| `slam_fixture_571` | `certified` | 5/8 | 0 | 8.76e+01 | 8.76e+01 | 1.12e-02 | — | 1.61e-05 | 1.81e-02 | 16.0 | 0.909 | v2026.8.17.1 |
+| `all` | `fnnls` | 0/81 | 0 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 8.05e-08 | 0.00e+00 | 8.63e-16 | 9.0 | 1.141 | v2026.8.17.1 |
+| `all` | `pdip_jacobi` | 29/81 | 0 | 2.73e+68 | 1.03e+68 | 3.36e+70 | 4.50e+68 | 1.56e+134 | 8.25e+61 | 19.0 | 1.219 | v2026.8.17.1 |
+| `all` | `pdip_raw` | 0/81 | 0 | 5.03e+08 | 2.19e-01 | 4.96e-02 | 3.31e-04 | 8.18e-16 | 3.24e-16 | 18.0 | 1.159 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-1` | 0/81 | 0 | 4.28e+11 | 7.15e+01 | 4.30e+01 | 3.02e-01 | 1.75e-11 | 1.80e-13 | 16.0 | 0.868 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-2` | 0/81 | 0 | 6.23e+10 | 2.70e+01 | 1.64e+01 | 1.15e-01 | 2.61e-12 | 2.66e-14 | 18.0 | 0.908 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-3` | 0/81 | 0 | 2.38e+10 | 1.00e+01 | 6.15e+00 | 4.37e-02 | 3.87e-13 | 3.93e-15 | 19.0 | 0.930 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_jaxnnls` | 75/81 | 0 | 4.30e+04 | 2.19e-01 | 7.40e-05 | 6.24e-07 | 8.18e-16 | 2.43e-16 | 50.0 | 2.119 | v2026.8.17.1 |
+| `all` | `pdip_raw_polish` | 0/81 | 0 | 5.03e+08 | 2.19e-01 | 4.96e-02 | 3.31e-04 | 8.18e-16 | 3.24e-16 | 23.0 | 1.223 | v2026.8.17.1 |
+| `all` | `certified` | 48/81 | 0 | 1.19e+02 | 1.19e+02 | 7.47e-02 | 1.49e-02 | 2.57e-04 | 1.81e-02 | 16.0 | 0.777 | v2026.8.17.1 |
+| `slam_fixture_571` | `fnnls` | 0/8 | 0 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 7.68e-08 | 0.00e+00 | 3.24e-16 | 7.0 | 1.066 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_jacobi` | 7/8 | 0 | 2.73e+68 | 1.03e+68 | 3.36e+70 | 4.50e+68 | 1.56e+134 | 8.25e+61 | 50.0 | 2.482 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw` | 0/8 | 0 | 6.91e+04 | 4.95e-06 | 4.93e-05 | 3.44e-07 | 3.60e-16 | 2.16e-16 | 18.0 | 1.366 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw_tol_1e-1` | 0/8 | 0 | 5.87e+07 | 9.08e+00 | 1.69e-02 | 1.45e-04 | 2.73e-12 | 3.60e-15 | 17.0 | 2.374 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw_tol_1e-2` | 0/8 | 0 | 2.24e+07 | 3.18e+00 | 6.44e-03 | 5.36e-05 | 4.14e-13 | 5.24e-16 | 18.0 | 1.378 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw_tol_1e-3` | 0/8 | 0 | 3.26e+06 | 9.54e-01 | 2.46e-03 | 1.52e-05 | 3.55e-14 | 4.20e-17 | 19.0 | 1.324 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw_tol_jaxnnls` | 8/8 | 0 | 2.09e-11 | 2.09e-11 | 3.05e-13 | 7.68e-08 | 6.01e-16 | 1.08e-16 | 50.0 | 2.471 | v2026.8.17.1 |
+| `slam_fixture_571` | `pdip_raw_polish` | 0/8 | 0 | 6.91e+04 | 4.95e-06 | 4.93e-05 | 3.44e-07 | 3.60e-16 | 2.16e-16 | 23.0 | 2.693 | v2026.8.17.1 |
+| `slam_fixture_571` | `certified` | 5/8 | 0 | 8.76e+01 | 8.76e+01 | 1.12e-02 | 3.53e-03 | 1.61e-05 | 1.81e-02 | 16.0 | 1.008 | v2026.8.17.1 |
 <!-- END auto-table:solver-accuracy -->
 
 ## Early stopping (latest run per corpus)
@@ -119,22 +119,22 @@ and the median warm wall-clock (`wall_ms`, 5 repeats after one compile call).
 <!-- BEGIN auto-table:solver-early-stopping -->
 | Corpus | Cap | Converged | Worst amp_rel_max | Worst amp_rel_max (sig) | Median amp_rel_max | Worst abs(flux_rel_source) | Worst abs(flux_inactive_rel) | Worst KKT | Median wall ms | Version |
 |--------|-----|-----------|-------------------|-------------------------|--------------------|----------------------------|------------------------------|-----------|----------------|---------|
-| `all` | 8 | 0/81 | 2.02e+14 | 3.43e+04 | 2.89e+01 | 6.18e+04 | 4.47e+02 | 3.19e-03 | 0.641 | v2026.8.17.1 |
-| `all` | 12 | 1/81 | 3.48e+13 | 8.96e+03 | 6.79e-01 | 3.14e+04 | 2.23e+02 | 5.71e-04 | 0.883 | v2026.8.17.1 |
-| `all` | 16 | 15/81 | 2.97e+13 | 1.49e+03 | 5.66e-06 | 2.69e+04 | 1.90e+02 | 2.47e-05 | 1.041 | v2026.8.17.1 |
-| `all` | 24 | 81/81 | 6.23e+10 | 2.70e+01 | 8.91e-08 | 1.64e+01 | 1.15e-01 | 2.66e-14 | 1.061 | v2026.8.17.1 |
-| `all` | 32 | 81/81 | 6.23e+10 | 2.70e+01 | 8.91e-08 | 1.64e+01 | 1.15e-01 | 2.66e-14 | 1.046 | v2026.8.17.1 |
-| `all` | 50 | 81/81 | 6.23e+10 | 2.70e+01 | 8.91e-08 | 1.64e+01 | 1.15e-01 | 2.66e-14 | 1.064 | v2026.8.17.1 |
-| `all` | 100 | 81/81 | 6.23e+10 | 2.70e+01 | 8.91e-08 | 1.64e+01 | 1.15e-01 | 2.66e-14 | 1.020 | v2026.8.17.1 |
-| `all` | 200 | 81/81 | 6.23e+10 | 2.70e+01 | 8.91e-08 | 1.64e+01 | 1.15e-01 | 2.66e-14 | 1.004 | v2026.8.17.1 |
-| `slam_fixture_571` | 8 | 0/8 | 3.00e+10 | 5.85e+03 | 5.14e+05 | 2.56e+01 | — | 3.02e-04 | 0.726 | v2026.8.17.1 |
-| `slam_fixture_571` | 12 | 0/8 | 2.76e+09 | 1.18e+03 | 1.24e+05 | 6.71e+00 | — | 4.14e-07 | 0.940 | v2026.8.17.1 |
-| `slam_fixture_571` | 16 | 1/8 | 5.87e+07 | 2.46e+01 | 2.67e+03 | 1.25e-01 | — | 6.11e-14 | 1.046 | v2026.8.17.1 |
-| `slam_fixture_571` | 24 | 8/8 | 2.24e+07 | 3.18e+00 | 1.82e+02 | 6.44e-03 | — | 5.24e-16 | 1.162 | v2026.8.17.1 |
-| `slam_fixture_571` | 32 | 8/8 | 2.24e+07 | 3.18e+00 | 1.82e+02 | 6.44e-03 | — | 5.24e-16 | 1.124 | v2026.8.17.1 |
-| `slam_fixture_571` | 50 | 8/8 | 2.24e+07 | 3.18e+00 | 1.82e+02 | 6.44e-03 | — | 5.24e-16 | 1.207 | v2026.8.17.1 |
-| `slam_fixture_571` | 100 | 8/8 | 2.24e+07 | 3.18e+00 | 1.82e+02 | 6.44e-03 | — | 5.24e-16 | 1.157 | v2026.8.17.1 |
-| `slam_fixture_571` | 200 | 8/8 | 2.24e+07 | 3.18e+00 | 1.82e+02 | 6.44e-03 | — | 5.24e-16 | 1.238 | v2026.8.17.1 |
+| `all` | 8 | 0/81 | 2.02e+14 | 3.43e+04 | 2.89e+01 | 6.18e+04 | 4.47e+02 | 3.19e-03 | 1.309 | v2026.8.17.1 |
+| `all` | 12 | 1/81 | 3.48e+13 | 8.96e+03 | 2.82e-01 | 3.14e+04 | 2.23e+02 | 5.71e-04 | 1.603 | v2026.8.17.1 |
+| `all` | 16 | 15/81 | 2.97e+13 | 1.49e+03 | 6.65e-11 | 2.69e+04 | 1.90e+02 | 2.47e-05 | 1.890 | v2026.8.17.1 |
+| `all` | 24 | 81/81 | 5.03e+08 | 2.19e-01 | 5.29e-11 | 4.96e-02 | 3.31e-04 | 3.24e-16 | 2.044 | v2026.8.17.1 |
+| `all` | 32 | 81/81 | 5.03e+08 | 2.19e-01 | 5.29e-11 | 4.96e-02 | 3.31e-04 | 3.24e-16 | 2.249 | v2026.8.17.1 |
+| `all` | 50 | 81/81 | 5.03e+08 | 2.19e-01 | 5.29e-11 | 4.96e-02 | 3.31e-04 | 3.24e-16 | 3.875 | v2026.8.17.1 |
+| `all` | 100 | 81/81 | 5.03e+08 | 2.19e-01 | 5.29e-11 | 4.96e-02 | 3.31e-04 | 3.24e-16 | 1.975 | v2026.8.17.1 |
+| `all` | 200 | 81/81 | 5.03e+08 | 2.19e-01 | 5.29e-11 | 4.96e-02 | 3.31e-04 | 3.24e-16 | 1.776 | v2026.8.17.1 |
+| `slam_fixture_571` | 8 | 0/8 | 3.00e+10 | 5.85e+03 | 5.14e+05 | 2.56e+01 | 1.51e-01 | 3.02e-04 | 1.951 | v2026.8.17.1 |
+| `slam_fixture_571` | 12 | 0/8 | 2.76e+09 | 1.18e+03 | 1.24e+05 | 6.71e+00 | 3.39e-02 | 4.14e-07 | 2.409 | v2026.8.17.1 |
+| `slam_fixture_571` | 16 | 1/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 1.08e-16 | 2.490 | v2026.8.17.1 |
+| `slam_fixture_571` | 24 | 8/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 2.16e-16 | 2.251 | v2026.8.17.1 |
+| `slam_fixture_571` | 32 | 8/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 2.16e-16 | 2.040 | v2026.8.17.1 |
+| `slam_fixture_571` | 50 | 8/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 2.16e-16 | 2.162 | v2026.8.17.1 |
+| `slam_fixture_571` | 100 | 8/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 2.16e-16 | 2.238 | v2026.8.17.1 |
+| `slam_fixture_571` | 200 | 8/8 | 6.91e+04 | 4.95e-06 | 1.04e+00 | 4.93e-05 | 3.44e-07 | 2.16e-16 | 2.562 | v2026.8.17.1 |
 <!-- END auto-table:solver-early-stopping -->
 
 ## Post-hoc exploration (latest run per corpus)
@@ -146,17 +146,17 @@ table is "Accuracy" above; the verdict it gave is in the
 <!-- BEGIN auto-table:solver-accuracy-posthoc -->
 | Corpus | Candidate | Unconverged | Worst abs(flux_inactive_rel) | Worst abs(flux_rel_source) | Worst amp_rel_max (sig) | Max active-set mismatch | Median iters | Max iters | Median wall ms | Version |
 |--------|-----------|-------------|------------------------------|----------------------------|-------------------------|-------------------------|--------------|-----------|----------------|---------|
-| `all` | `fnnls` | 0/81 | 8.05e-08 | 0.00e+00 | 0.00e+00 | 0 | 9.0 | 20 | 1.092 | v2026.8.17.1 |
-| `all` | `pdip_raw` | 0/81 | 1.15e-01 | 1.64e+01 | 2.70e+01 | 26 | 18.0 | 24 | 0.991 | v2026.8.17.1 |
-| `all` | `pdip_raw_polish` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 1.203 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_jaxnnls` | 75/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 50.0 | 50 | 2.201 | v2026.8.17.1 |
-| `all` | `pdip_raw_polish_cap20` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 1.385 | v2026.8.17.1 |
-| `all` | `pdip_raw_polish_cap50` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 1.259 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_jaxnnls_cap100` | 74/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 100.0 | 100 | 5.014 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_jaxnnls_cap200` | 74/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 200.0 | 200 | 7.541 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-4` | 0/81 | 1.65e-02 | 2.30e+00 | 3.52e+00 | 26 | 20.0 | 26 | 1.180 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-5` | 0/81 | 2.36e-03 | 3.37e-01 | 2.27e-01 | 25 | 21.0 | 28 | 1.187 | v2026.8.17.1 |
-| `all` | `pdip_raw_tol_1e-6` | 78/81 | 8.05e-08 | 7.49e-10 | 1.25e-10 | 0 | 50.0 | 50 | 2.355 | v2026.8.17.1 |
+| `all` | `fnnls` | 0/81 | 8.05e-08 | 0.00e+00 | 0.00e+00 | 0 | 9.0 | 20 | 2.616 | v2026.8.17.1 |
+| `all` | `pdip_raw` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 18.0 | 24 | 1.542 | v2026.8.17.1 |
+| `all` | `pdip_raw_polish` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 1.860 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_jaxnnls` | 75/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 50.0 | 50 | 3.176 | v2026.8.17.1 |
+| `all` | `pdip_raw_polish_cap20` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 2.199 | v2026.8.17.1 |
+| `all` | `pdip_raw_polish_cap50` | 0/81 | 3.31e-04 | 4.96e-02 | 2.19e-01 | 23 | 23.0 | 30 | 2.038 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_jaxnnls_cap100` | 74/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 100.0 | 100 | 5.826 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_jaxnnls_cap200` | 74/81 | 6.24e-07 | 7.40e-05 | 2.19e-01 | 4 | 200.0 | 200 | 11.559 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-4` | 0/81 | 1.65e-02 | 2.30e+00 | 3.52e+00 | 26 | 20.0 | 26 | 1.889 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-5` | 0/81 | 2.36e-03 | 3.37e-01 | 2.27e-01 | 25 | 21.0 | 28 | 1.605 | v2026.8.17.1 |
+| `all` | `pdip_raw_tol_1e-6` | 78/81 | 8.05e-08 | 7.49e-10 | 1.25e-10 | 0 | 50.0 | 50 | 3.681 | v2026.8.17.1 |
 <!-- END auto-table:solver-accuracy-posthoc -->
 
 ## Euclid latent by candidate (post-hoc)
@@ -166,13 +166,13 @@ computed by the euclid pipeline's own latent code ([`euclid_latent.py`](./euclid
 test passes at rel 1e-3 against the eager NumPy value.
 
 <!-- BEGIN auto-table:solver-euclid-latent -->
-_System `euclid_vis_lp/euclid_vis_lp_k0`; eager NumPy `total_source_flux` 3.3198794874035915, released jit 3.511093374207152; validation passed (x_ref rel -3.26e-08, pdip_raw rel -2.00e-08); the euclid test's rtol is 0.001._
+_System `euclid_vis_lp/euclid_vis_lp_k0`; eager NumPy `total_source_flux` 3.3198794874035915, released jit 3.511093374207152, library-main jit 3.320127603567922; validation passed (x_ref rel -3.26e-08, pdip_raw rel -2.00e-08); the euclid test's rtol is 0.001._
 
 | Candidate | Post-hoc | total_source_flux | rel vs eager | Within test rtol | Converged | Iterations | Version |
 |-----------|----------|-------------------|--------------|------------------|-----------|------------|---------|
 | `fnnls` | no | 3.3198794 | -3.26e-08 | yes | True | 8 | v2026.8.17.1 |
 | `pdip_jacobi` | no | 3.3202250 | +1.04e-04 | yes | True | 19 | v2026.8.17.1 |
-| `pdip_raw` | no | 3.5110933 | +5.76e-02 | no | True | 24 | v2026.8.17.1 |
+| `pdip_raw` | no | 3.3201275 | +7.47e-05 | yes | True | 24 | v2026.8.17.1 |
 | `pdip_raw_tol_1e-1` | no | 3.8766845 | +1.68e-01 | no | True | 23 | v2026.8.17.1 |
 | `pdip_raw_tol_1e-2` | no | 3.5110933 | +5.76e-02 | no | True | 24 | v2026.8.17.1 |
 | `pdip_raw_tol_1e-3` | no | 3.3756725 | +1.68e-02 | no | True | 25 | v2026.8.17.1 |
diff --git a/scripts/lens/solver/_solvers.py b/scripts/lens/solver/_solvers.py
index ccefa6eb3..6352c61b5 100644
--- a/scripts/lens/solver/_solvers.py
+++ b/scripts/lens/solver/_solvers.py
@@ -356,9 +356,12 @@ def add(c: Candidate):
     add(
         _jax_candidate(
             "pdip_raw",
-            "Released default for linear-object-only (MGE) inversions: forward PDIP on the raw "
-            "(Q, q) at data_scaled_solver_tol(q), cap 50; the library returns y = x / D and the "
-            "reconstruction y * D, reproduced exactly. Extras: the backward-pass polish / "
+            "Library default for linear-object-only (MGE) inversions: forward PDIP on the raw "
+            "(Q, q) at data_scaled_solver_tol(q), cap 50; the library maps the iterate to "
+            "y = x / D and, since PyAutoArray#595, returns the #573 polish of it (at most "
+            "RAW_POLISH_MAX_ITER tight PDIP iterations on (Q_pc, q_pc), kept iff converged and "
+            "interior) — reconstruction y * D, reproduced exactly. converged / iterations are "
+            "the raw forward solve's (the polish is not counted). Extras: the polish / "
             "relaxed-KKT status from raw_forward_backward_status (not timed).",
             "autoarray.util.jax_nnls.solve_nnls_primal_raw_forward (+ raw_forward_backward_status)",
             _build_pdip_raw(50),
@@ -372,7 +375,8 @@ def add(c: Candidate):
                 f"Raw forward PDIP with the data-scaled tolerance at factor {label} in place of "
                 f"DATA_SCALED_TOL_FACTOR (tol = {label} * n * eps * max(1, max|q|), i.e. "
                 "data_scaled_solver_tol(q) * factor / DATA_SCALED_TOL_FACTOR), cap 50. The "
-                "1e-2 row reproduces the released tolerance through solve_nnls directly.",
+                "1e-2 row reproduces the released tolerance through solve_nnls directly, with "
+                "no polish (so, since PyAutoArray#595, not pdip_raw's value).",
                 "autoarray.util.jax_nnls.solve_nnls + data_scaled_solver_tol",
                 _build_pdip_raw_tol(factor),
             )
diff --git a/scripts/lens/solver/euclid_latent.py b/scripts/lens/solver/euclid_latent.py
index 3b89cdde3..4f1d9090a 100644
--- a/scripts/lens/solver/euclid_latent.py
+++ b/scripts/lens/solver/euclid_latent.py
@@ -23,8 +23,11 @@
 injected amplitudes into the source image summed on the uniform over-sample-4 grid.
 
 Validation (gates publication): injecting the stored fnnls ``x_ref`` must reproduce the corpus
-``latent_reference.eager_numpy`` and injecting ``pdip_raw``'s ``x`` its ``jit`` value, both to
-rel 1e-6. If either fails the cell writes nothing and exits 1.
+``latent_reference.eager_numpy`` and injecting ``pdip_raw``'s ``x`` the jitted latent of the
+library it runs against (``PDIP_RAW_JIT_EXPECTED``), both to rel 1e-6. If either fails the cell
+writes nothing and exits 1. Phase 2 (PyAutoArray#595) re-based the second leg: the corpus
+``latent_reference.jit`` (3.511093374, +5.76 %) is the capture-time *released* library's value
+and stays in the manifest as a record; library main now returns the polished iterate.
 
 Run from the repo root (needs the euclid pipeline checkout beside this repo, and its dataset)::
 
@@ -78,6 +81,20 @@ def _profiling_root() -> Path:
 VALIDATION_RTOL = 1.0e-6
 TEST_RTOL = 1.0e-3
 
+# The jitted ``total_source_flux`` the ``pdip_raw -> jit`` validation leg must reproduce.
+# Re-based in phase 2 for PyAutoArray#595 (merged 2026-09-30, merge 7a89e19a0): the jit path
+# (``solve_nnls_primal_raw_forward``) now returns the #573 polished iterate instead of the raw
+# forward stop, so injecting ``pdip_raw``'s ``x`` no longer reproduces the capture-time value
+# ``latent_reference.jit`` = 3.511093374207152 (+5.76e-2 vs eager, the released library before
+# #595), which this constant replaces. Value: the euclid test's own jitted
+# ``LatentEuclid.variables`` on library main 7a89e19a0, euclid pipeline 26e4385b, measured
+# 2026-09-30 (+7.47e-5 vs eager). The manifest keeps the old value as the capture record.
+PDIP_RAW_JIT_EXPECTED = 3.320127603567922
+PDIP_RAW_JIT_SOURCE = (
+    "jitted LatentEuclid.variables on PyAutoArray main 7a89e19a0 (post-#595), "
+    "euclid pipeline 26e4385b, 2026-09-30; replaces the pre-#595 released jit 3.511093374207152"
+)
+
 
 def _euclid_fit_factory():
     """``x -> total_source_flux`` through the euclid pipeline's own eager latent code."""
@@ -137,11 +154,12 @@ def main() -> int:
     (system,) = [s for s in _corpus.load_corpus([GROUP]) if s.name == SYSTEM]
     group = next(g for g in _corpus.read_manifest()["groups"] if g["name"] == GROUP)
     ref = group["source"]["latent_reference"]
-    eager, jit = float(ref["eager_numpy"]), float(ref["jit"])
+    eager, jit_released = float(ref["eager_numpy"]), float(ref["jit"])
+    jit = PDIP_RAW_JIT_EXPECTED
 
     total_source_flux, info = _euclid_fit_factory()
 
-    # --- validation: x_ref -> eager, pdip_raw -> jit --------------------------------------
+    # --- validation: x_ref -> eager, pdip_raw -> jit (library main, post-#595) -------------
     flux_ref, n_ref = total_source_flux(system.x_ref)
     x_raw, _ = _solvers.CANDIDATES["pdip_raw"].fn(system.Q, system.q, system.meta)
     flux_raw, n_raw = total_source_flux(x_raw)
@@ -157,6 +175,7 @@ def main() -> int:
             "expected": jit,
             "rel": (flux_raw - jit) / jit,
             "stub_calls": n_raw,
+            "expected_source": PDIP_RAW_JIT_SOURCE,
         },
         "rtol": VALIDATION_RTOL,
     }
@@ -207,7 +226,11 @@ def main() -> int:
         "posthoc": "POST-HOC, NOT PRE-REGISTERED: an input to the phase-2 fix, not a verdict.",
         "system": f"{GROUP}/{SYSTEM}",
         "latent_key": "total_source_flux",
-        "latent_reference": {"eager_numpy": eager, "jit_released": jit},
+        "latent_reference": {
+            "eager_numpy": eager,
+            "jit_released": jit_released,
+            "jit_library_main": jit,
+        },
         "method": "reconstruction injected through inversion_util.reconstruction_positive_only_from "
         "into the eager NumPy fit; total_source_flux from "
         "LatentEuclid._source_flux_latents_on_uniform_grid",
diff --git a/scripts/misc/tooling/build_readme.py b/scripts/misc/tooling/build_readme.py
index 5f5ef94f8..0d2324d18 100644
--- a/scripts/misc/tooling/build_readme.py
+++ b/scripts/misc/tooling/build_readme.py
@@ -814,7 +814,13 @@ def _render_solver_euclid_latent_table() -> str:
     rtol = data.get("test_rtol")
     rows = [
         f"_System `{data.get('system')}`; eager NumPy `total_source_flux` "
-        f"{ref.get('eager_numpy')}, released jit {ref.get('jit_released')}; validation "
+        f"{ref.get('eager_numpy')}, released jit {ref.get('jit_released')}"
+        + (
+            f", library-main jit {ref['jit_library_main']}"
+            if ref.get("jit_library_main") is not None
+            else ""
+        )
+        + "; validation "
         f"{'passed' if val.get('passed') else 'FAILED'} (x_ref rel "
         f"{_sci((val.get('x_ref_to_eager') or {}).get('rel'))}, pdip_raw rel "
         f"{_sci((val.get('pdip_raw_to_jit') or {}).get('rel'))}); the euclid test's rtol is "
diff --git a/wiki/campaigns/linear_solver_accuracy.md b/wiki/campaigns/linear_solver_accuracy.md
index c7b5186ff..cdcf349c9 100644
--- a/wiki/campaigns/linear_solver_accuracy.md
+++ b/wiki/campaigns/linear_solver_accuracy.md
@@ -1,15 +1,15 @@
 # Linear-solver accuracy
 
-**Status:** open
+**Status:** open (phase 2 shipped)
 **Question:** On the positive-only systems real models produce, does each solver return the fnnls reference amplitudes, does its convergence flag tell the truth, and what does each digit of accuracy cost in iterations?
 **Pre-registered rule:** a candidate is admissible only if it reports converged on 100 % of the 81 systems, has significant-column error ≤ 1e-3 and source-flux error ≤ 1e-4 everywhere (≤ 1e-4 on the euclid system), and has a KKT residual within 10x `pdip_jacobi`'s; lowest median iterations wins ([ledger, pre-registered rule](../../results/notes/linear_solver_accuracy_2026_09.md#pre-registered-decision-rule); committed at `327f571` before the deciding run).
-**Verdict:** phase 1: no drop-in candidate; phase 2 needs a solution-based stop. No candidate is admissible. The released raw stop is blind to the flux on reference-inactive columns.
+**Verdict:** phase 1: no drop-in candidate; phase 2 needs a solution-based stop. No candidate is admissible. The released raw stop is blind to the flux on reference-inactive columns. Phase 2: PyAutoArray#595 shipped the forward polish (the phase-1 `pdip_raw_polish`, bit-for-bit on 81/81): `flux_inactive_rel` 0.115 -> 3.31e-4, euclid latent +5.76e-2 -> +7.47e-5 (test green); still not admissible under the rule as written (criteria 2–4, for the recorded rule weaknesses).
 **Headline:** the released raw PDIP solve reports converged on 81/81 systems, yet on the euclid system it leaves 11.5 % of the reference's total amplitude on columns the reference holds at zero (`total_source_flux` +5.76 %); laptop WSL CPU, no RAL job.
-**Library PRs:** none yet (phase 2: `draft/bug/autoarray/raw_pdip_forward_amplitude_bias_fix.md`).
-**Profiling PRs:** #354 (this study; PR number pending).
+**Library PRs:** PyAutoArray#595 (merged 2026-09-30, merge `7a89e19a0`; not yet released).
+**Profiling PRs:** #354 (phase 1); phase 2 record (PR pending).
 **Ledger:** [linear_solver_accuracy_2026_09.md](../../results/notes/linear_solver_accuracy_2026_09.md)
 **Mind contract:** epic `linear-solver-programme`; `active/raw_forward_pdip_nnls_early_stopping.md`
-**Next:** phase 2: a solution-based stop (an active-set certificate on the PDIP iterate) in PyAutoArray, gated on `flux_inactive_rel` and the euclid latent directly.
+**Next:** phase 3 (GPU / vmap / A100 timing of the polished call); a solution-based stop (option 1) remains unbuilt; re-base the rule (latent-based criterion 3, absolute KKT floor) before its next use.
 
 ## Why this campaign
 
@@ -45,7 +45,7 @@ Prior art: the [NNLS solver ledger](../../results/notes/nnls_solver_ledger.md),
 | Phase | Dates | Question | Pre-registered rule | Result | Jobs | PRs |
 |---|---|---|---|---|---|---|
 | 1 accuracy study | 2026-09-30 | Is any existing solver or constant change a drop-in fix, on 81 captured systems? | admissibility on flag, significant-column and source-flux accuracy, euclid proxy, KKT; `327f571` | no candidate admissible; the released stop is blind to flux on reference-inactive columns | none (laptop) | #354 |
-| 2 PyAutoArray fix | not started | A solution-based stop for the raw forward PDIP | to be written before its deciding run | — | — | `draft/bug/autoarray/raw_pdip_forward_amplitude_bias_fix.md` |
+| 2 PyAutoArray fix | 2026-09-30 | Fix the raw forward PDIP's amplitude bias | phase-1 rule re-applied, not re-based | forward polish shipped: `flux_inactive_rel` ≤ 3.31e-4, euclid latent +7.47e-5, 0/81 unconverged; rule still FAILs 2–4 ([ledger](../../results/notes/linear_solver_accuracy_2026_09.md#phase-2-2026-09-30--library-fix-shipped-pyautoarray595)) | none (laptop) | PyAutoArray#595 |
 | 3 GPU / vmap / A100 | not started | The same cells under `jit(vmap)` on the A100 | — | — | — | `draft/research/autoarray/mge_nnls_fix_pyautoarray_571_slam_60.md` |
 | standing | every release | Re-run `accuracy.py` (and `early_stopping.py`) over the whole corpus | no drift in the tables without a solver change | — | — | — |
 
@@ -53,18 +53,16 @@ Prior art: the [NNLS solver ledger](../../results/notes/nnls_solver_ledger.md),
 
 | PR | What | Merge | Release |
 |---|---|---|---|
-| none yet | — | — | — |
+| PyAutoArray#595 | forward value of the raw PDIP mode = the #573 polished iterate | 2026-09-30 (`7a89e19a0`) | pending |
 
-The package itself (`scripts/lens/solver/`) is profiling-repo code in #354; no library change has
-come out of the campaign yet.
+The package itself (`scripts/lens/solver/`) is profiling-repo code in #354. PyAutoArray#595 is the
+campaign's first library change.
 
 ## Open / parked / drafts
 
-- `draft/bug/autoarray/raw_pdip_forward_amplitude_bias_fix.md` — phase 2. The ledger's
-  "What phase 2 should implement" ranks the options with measured iteration costs: a
-  solution-based stop (the only one supported on every system; its cost is not yet measured),
-  the forward polish as a stop-gap (euclid latent +7.5e-5, median 23 iterations), a tighter
-  tolerance or larger cap (does not fix the flag).
+- Option 1 of the ledger's "What phase 2 should implement" — a solution-based stop (active-set
+  certificate) — is still unbuilt; #595 shipped option 2 (the forward polish). Needed only if
+  better than `flux_inactive_rel` 3.3e-4 is required on euclid-like systems.
 - `draft/research/autoarray/mge_nnls_fix_pyautoarray_571_slam_60.md` — GPU / vmap / A100 rows.
 - A phase-2 rule should give the KKT criterion an absolute floor (as written, the reference fnnls
   fails it on 9/52 systems at residuals ≤ 4.3e-16).
@@ -109,3 +107,19 @@ tolerance and jaxnnls's tolerance above cap 50 (51 iterations). Next: phase 2 in
 
 Also in #354: `xla_attribution.py`'s PyAutoArray line anchors were re-pinned to `d4298445`
 after PyAutoArray#591 moved `inversion/inversion/abstract.py` by +1 / +9.
+
+### 2026-09-30 — phase 2: PyAutoArray#595 shipped the forward polish
+
+PyAutoArray#595 (issue #594, merge `7a89e19a0`) makes the raw mode return the #573 polished
+iterate as its forward value. All cells were re-run against the merged library (provenance
+PyAutoArray `7a89e19a0` in every JSON): `pdip_raw` is now identical to phase 1's `pdip_raw_polish`
+on 81/81 systems — 0/81 unconverged, forward iterations median 18 / max 24 (the polish's 1–7 are
+not counted), worst `flux_inactive_rel` 3.31e-4 (was 0.115), worst source-flux 4.96e-2 (was 16.4,
+euclid only), KKT ≤ 3.2e-16. Euclid `total_source_flux` +7.47e-5 (was +5.76e-2), so the pipeline
+test is green (19/19). The rule as written still rejects it (criterion 2 on euclid and the
+`noise_x3_v32` flat direction, criterion 3's proxy at 4.96e-2 against a latent of 7.5e-5,
+criterion 4 at the 3e-16 floor on 5/52). `euclid_latent.py`'s `pdip_raw -> jit` validation leg
+was re-based to the library-main jit value 3.320127604; `pdip_raw_tol_1e-2` (no polish) now
+reproduces the *pre-fix* `pdip_raw`, and the phase-1 sentences saying otherwise are annotated.
+The `_v2026.8.17.1` artefacts were overwritten in place (source-checkout version stamp); the
+pre-fix ones are at profiling `3ad68af`. [Ledger, phase 2](../../results/notes/linear_solver_accuracy_2026_09.md#phase-2-2026-09-30--library-fix-shipped-pyautoarray595).
diff --git a/wiki/index.md b/wiki/index.md
index cd4f4511f..b1d910356 100644
--- a/wiki/index.md
+++ b/wiki/index.md
@@ -16,7 +16,7 @@ why, phases with their pre-registered rules, tag-verified release tables, caveat
 | [Point-source image-plane CPU](campaigns/point_source_image_plane_cpu.md) | Where does the single-source image-plane `PointSolver` likelihood spend CPU time, and what cuts it? | open | IP-4b 1.44x per call, 8490H job 357321 (loaded node); cumulative 24.69 → 2.095 ms indicative only | IP-2, IP-3, IP-4b accepted; IP-4c +6.2 % accepted for correctness (human) | PyAutoArray#569, #570, PyAutoLens#749 (2026.9.26.1); PyAutoArray#580, #584, PyAutoLens#753 (unreleased) | #293 #298 #301 #305 #321 #330 #335 | [point_source_cpu_campaign](../results/notes/point_source_cpu_campaign.md), [shared breakdown](../results/notes/point_source_shared_likelihood_breakdown.md) | `point-source-cpu-speed`; `complete/2026/09/point-source-cpu-p4.md` | extent/scale lever (2.37x, not shipped) in drafts | — | 2026-09-27 |
 | [Point-source source-plane](campaigns/point_source_source_plane.md) | Can the single-source source-plane χ² likelihood and its gradient be made cheaper? | parked | forward-mode gradient 0.36–0.58x of reverse step; L24 compile 95.9 → 11.2 s, EPYC job 359192 | 2a NO-GO; 2b GO (host re-based); 2c no crossover to n=24; gradient_mode shipped | PyAutoFit#1649, PyAutoLens#752 (released 2026.9.27.2) | #317 #318 #323 #327 #331 #336 #349 | [point_source_source_plane_campaign](../results/notes/point_source_source_plane_campaign.md) | `point-source-cpu-speed`; `draft/research/autolens_profiling/point_source_source_plane_chi_squared_speed.md` | parked: blackjax forward mode (needs autolens_inference point-source search leaf); A100 vmap row done (5.6 µs/call b64, jobs 366912/366914) | — | 2026-09-28 |
 | [Interferometer likelihood](campaigns/interferometer_likelihood.md) | Where do interferometer MGE and mesh likelihoods spend time on A100 and CPU, and which levers pay? | open | alma MGE A100 939.6 ms → 2.69 ms (W~ route) | W~ route and real scatter shipped; mesh CPU gate stays 60 | PyAutoArray#576 (+ PyAutoGalaxy#629, PyAutoLens#750), #578 (2026.9.27.1); PyAutoArray#540, #541, #544, #545 (2026.9.11.1); PyAutoArray#582 (unreleased) | #312 #313 #319 #324 #328 #333 | [MGE](../results/notes/interferometer_mge_breakdown_2026_09.md), [mesh A100](../results/notes/interferometer_mesh_a100_breakdown_2026_09.md), [mesh CPU](../results/notes/interferometer_mesh_cpu_breakdown_2026_09.md) | `interferometer-likelihood-campaign` (not in epics.md); `draft/research/autolens_profiling/interferometer_mesh_breakdown_numba_cpu_decision_matrix.md` | phase 4 decision matrix (first add sma / alma_high CPU radius rows at r2.0 / r5.0) | — | 2026-09-28 |
-| [Linear-solver accuracy](campaigns/linear_solver_accuracy.md) | Do the positive-only solvers return the fnnls amplitudes on real systems, does the converged flag tell the truth, and at what iteration cost? | open | released raw PDIP converged 81/81 yet 11.5 % of reference amplitude on reference-inactive columns (euclid, `total_source_flux` +5.76 %), laptop WSL CPU, no RAL job | phase 1 (rule `327f571`): no drop-in candidate; phase 2 needs a solution-based stop | none yet (phase 2: `draft/bug/autoarray/raw_pdip_forward_amplitude_bias_fix.md`) | #354 (PR pending) | [linear_solver_accuracy_2026_09](../results/notes/linear_solver_accuracy_2026_09.md) | `linear-solver-programme`; `active/raw_forward_pdip_nnls_early_stopping.md` | phase 2: solution-based stop in PyAutoArray | — | 2026-09-30 |
+| [Linear-solver accuracy](campaigns/linear_solver_accuracy.md) | Do the positive-only solvers return the fnnls amplitudes on real systems, does the converged flag tell the truth, and at what iteration cost? | open | released raw PDIP converged 81/81 yet 11.5 % of reference amplitude on reference-inactive columns (euclid, `total_source_flux` +5.76 %); after PyAutoArray#595 3.31e-4 and +7.47e-5; laptop WSL CPU, no RAL job | phase 1 (rule `327f571`): no drop-in candidate; phase 2: forward polish shipped (#595), still not admissible under the rule as written | PyAutoArray#595 (merged, pending release) | #354; phase 2 (PR pending) | [linear_solver_accuracy_2026_09](../results/notes/linear_solver_accuracy_2026_09.md) | `linear-solver-programme`; `active/raw_forward_pdip_nnls_early_stopping.md` | phase 3: GPU / vmap timing; solution-based stop still unbuilt | — | 2026-09-30 |
 | [Cluster PointSolver](campaigns/cluster_pointsolver.md) | What does a representative cluster PointSolver likelihood cost, and which levers pay at cluster scale? | draft | none yet (carried: 5.2x cluster from static lattice) | not started | none | none | none yet; carried evidence in [point_source_cpu_campaign](../results/notes/point_source_cpu_campaign.md) | `cluster-pointsolver-speed`; `draft/research/autolens_profiling/cluster_pointsolver_speed.md` | phase 1: cluster data + released-code breakdown | — | 2026-09-27 |
 
 ## Closed, parked and draft campaigns