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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/C/COMPLETE

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/C/accuracy_by_condition.csv

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/C/protocol.json

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+    "path": "/nfs/ywang29/GmNet/runs/e4_alignment/e4a_s3_channel_derangement_seed0/official_eval/results.json",
+    "sha256": "dd81aec890b9ba79b8a8d3cf2427f81d583615e61f839407093083c96dec5a8a"
+  },
+  "protocol": {
+    "path": "/nfs/ywang29/GmNet/e4_spectral_audit/protocol/IMAGENET_MATCHED_SPECTRAL_AUDIT_PROTOCOL.md",
+    "sha256": "b66f0aa2f620135584483fb0057fe6fec9a55b4b501b08fd26065034a892b4a2"
+  },
+  "runner": {
+    "path": "/nfs/ywang29/GmNet/e4_spectral_audit/code/run_e4_imagenet_spectral_audit.py",
+    "sha256": "2b56465319e651716170fbaa58d43f14924d70f694b0a273ab63255c2697edfe"
+  }
+}

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/C/spectral_by_block.csv

@@ -0,0 +1,171 @@
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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/C/spectral_by_stage.csv

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/COMPLETE

@@ -0,0 +1 @@
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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/artifacts.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/batch_pairing_manifest.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/checkpoint_manifest.json

@@ -0,0 +1,572 @@
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+}

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/environment.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/protocol.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/results.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D/source_provenance.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/COMPLETE

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+complete

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/accuracy_by_condition.csv

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/artifacts.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/checkpoint_manifest.json

@@ -0,0 +1,572 @@
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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/protocol.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/results.json

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+task_id,arm,order,basis,global_block_index,stage,block,signal,psd_high_fraction,psd_centroid,psd_entropy,valid,invalid,centered_rms,log10_centered_energy,log10_centered_energy_valid,log10_centered_energy_invalid,noncentered_rms,gate_mean,gate_cv,gate_zero_fraction,gate_active_fraction,gate_clip_crossing_fraction,rho_used,rho_response,jsd,jsd_valid_channels,m_res,m_res_conditional,m_res_conditional_valid_channels,suppressed_fraction,constant_gate_fraction,constant_gate_count,output_minus_carrier_high_fraction,output_minus_carrier_centroid,output_minus_carrier_valid_channels,m_off,m_disp,source_valid,source_suppressed,source_valid_fraction,source_suppressed_fraction
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+D_order1,D,salt1,fft,0,1,1,gate_input,0.28505247202254846,0.3441693548156834,0.8106101736815973,786432,0,1.0490003155866243,3.530719884983454,786432,0,1.4537301535559952,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,0,1,1,gate_value,0.34495936435526964,0.3839039121331347,0.8325617691278913,640311,146121,0.36476170237485744,2.8054239124241196,648275,138157,0.39987710211710403,0.1152894194640653,1.8530041897574518,0.8757167738311145,0.11503305240553252,0.0,,,,,,,,,0.18580245971679688,146121,,,,,,,,,
+D_order1,D,salt1,fft,0,1,1,gated_product,0.4036667969474299,0.42363965007713855,0.8683249139027405,648275,138157,0.49710701689326653,3.0733198326579645,648275,138157,0.5012811581226748,,,,,,0.2547395524823327,0.20949273428754708,0.28759643592499906,648275,0.6326501286167657,0.7674412308294881,648275,0.17567571004231772,,,0.11046945618685386,0.07433327444789813,648275,0.407184233282031,0.13377927133075854,3145728,552628,1.0,0.1756734375
+D_order1,D,salt1,fft,0,1,1,residual_delta,0.4124410623807789,0.4344905867893401,0.8052602456067519,196318,290,0.0016077012921166137,-3.088532082360711,196318,290,0.0017795469282276708,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,1,1,2,carrier,0.2570151834825535,0.32218751266289547,0.7798499178018904,786432,0,0.7893175296114542,3.28500059919753,786432,0,1.1127797642354276,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,1,1,2,gate_input,0.2569523480408774,0.3222670772214958,0.7801575407002164,786432,0,0.790321082945024,3.2860815549905693,786432,0,1.1139851320540815,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,1,1,2,gate_value,0.3272832938817358,0.37108215108969844,0.8168055868022694,730493,55939,0.2843682007040117,2.4480274755208775,730493,55939,0.30504937417273537,0.08828407673328911,2.4675709807955264,0.827536563484036,0.17245888223453443,0.0,,,,,,,,,0.07113011678059894,55939,,,,,,,,,
+D_order1,D,salt1,fft,1,1,2,gated_product,0.3867160292519947,0.4116466720254942,0.8629007553815649,730493,55939,0.2809019934316207,2.434167700910694,730493,55939,0.2846027060604073,,,,,,0.26505184499918116,0.2056388997286143,0.2999062571184127,730493,0.7375582175565211,0.7940022177675993,730493,0.07113011678059897,,,0.12273765154666792,0.08430203366891445,730493,0.46659492011879217,0.15399500374957986,3145728,223756,1.0,0.07113125000000001
+D_order1,D,salt1,fft,1,1,2,residual_delta,0.3353298842710113,0.3815757414418867,0.7393665241475862,191816,4792,7.466478375984256e-05,-4.351746763897295,191816,4792,0.0006960903230365276,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,2,1,3,carrier,0.18282651967786467,0.2757263006689329,0.7293348870600562,786432,0,1.6110961563328994,3.9085021318445827,786432,0,4.508179021403911,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,2,1,3,gate_input,0.1819720736261622,0.2750579188643103,0.7284604787596529,786432,0,1.6086843319088477,3.907191499434569,786432,0,4.507482044497891,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,2,1,3,gate_value,0.3277409829973541,0.3721604923625419,0.7955556931532348,639643,146789,0.18720341610921537,2.2029425543376027,639643,146789,0.1890044620888553,0.025370751011046053,6.02703064783154,0.973576292699697,0.022689313304667568,0.0,,,,,,,,,0.18665186564127603,146789,,,,,,,,,
+D_order1,D,salt1,fft,2,1,3,gated_product,0.40537253035181486,0.42192676761326636,0.8625207428091991,639643,146789,0.27556248977063014,2.581333470671676,639643,146789,0.2760464503702379,,,,,,0.4948706332556956,0.3972181264787328,0.38516445930357046,639643,0.6753025051154227,0.8302663803868329,639643,0.18665186564127603,,,0.23502802309704385,0.1563193287833005,639643,0.47074948385695803,0.1534596997179442,3145728,587156,1.0,0.18665390624999997
+D_order1,D,salt1,fft,2,1,3,residual_delta,0.4214810002722144,0.42514256331577127,0.7505094587092027,196478,130,6.899639793154114e-05,-4.914472744548131,196482,126,0.05094806137754999,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,3,2,1,carrier,0.13081589829132292,0.23146805525894948,0.6703090027486822,1572864,0,3.153738740846298,3.889564925294232,1572864,0,8.591231366403278,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,3,2,1,gate_input,0.13097749597616692,0.2315739379932833,0.6700504237830525,1572864,0,3.145059327465005,3.887080668660027,1572864,0,8.579738818981035,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,3,2,1,gate_value,0.4097574334905109,0.40846570634731405,0.8379736484685099,1057857,515007,0.18580698396128664,2.1039132446515,1101932,470932,0.2343037456864293,0.02628846473038319,3.9878760133705606,0.9793262092434629,0.01585014498963648,0.0,,,,,,,,,0.32743263244628906,515007,,,,,,,,,
+D_order1,D,salt1,fft,3,2,1,gated_product,0.4676362300164957,0.4474545659858705,0.8794898096414796,1101932,470932,0.586747232705922,3.1572691730878613,1101932,470932,0.5890951704118683,,,,,,0.5335258487734952,0.36393713879031997,0.418596814761968,1101932,0.5817430608123558,0.8300846840412462,1101932,0.2994105021158854,,,0.3348663239353532,0.21822759261209645,1101932,0.4008159066582353,0.13781104613883854,6291456,1883728,1.0,0.299419921875
+D_order1,D,salt1,fft,3,2,1,residual_delta,0.25661064560212854,0.3108477941215031,0.7006421248469613,393170,46,0.00037554777522130924,-3.976540349284951,393170,46,0.0028510075408584756,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,4,2,2,carrier,0.1832876272215878,0.2776086878773357,0.71602346271025,1572864,0,2.9592522266362664,3.8320530674954867,1572864,0,8.64804295838103,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,4,2,2,gate_input,0.18334426940253687,0.27763493726612976,0.7158560554711974,1572864,0,2.951866865917512,3.829893209782165,1572864,0,8.637911897378997,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,4,2,2,gate_value,0.3875058211145723,0.4096200206539564,0.7906619297953577,1169145,403719,0.17241305809615837,1.8843605660231502,1224657,348207,0.2175516668415366,0.021801314047744318,5.374254870361601,0.984155460279815,0.007317367865114794,0.0,,,,,,,,,0.25667762756347656,403719,,,,,,,,,
+D_order1,D,salt1,fft,4,2,2,gated_product,0.4276657204703266,0.43494330715445306,0.8306055521369087,1224657,348207,0.5852430739676648,2.979457105680371,1224657,348207,0.5868587480617822,,,,,,0.4474698987885599,0.38261458399048126,0.3470159794481981,1224657,0.6013212348903825,0.7717365007619343,1224657,0.22138404846191406,,,0.24573258150432836,0.16107375055488043,1224657,0.48347826761773327,0.16259446841911596,6291456,1392828,1.0,0.22143307291666667
+D_order1,D,salt1,fft,4,2,2,residual_delta,0.286694543921307,0.3387929273343027,0.6646807235919368,393137,79,0.05247895394500529,0.3242019780733793,393137,79,0.09848524221415131,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,5,2,3,carrier,0.1695811663865936,0.26142071596403826,0.6964454424365525,1572864,0,3.8181585064838526,4.054114691354383,1572864,0,10.210965699635947,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,5,2,3,gate_input,0.16933850892457072,0.2613386135353698,0.6964058075095025,1572864,0,3.8104136019409207,4.052321199672278,1572864,0,10.201023281893775,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,5,2,3,gate_value,0.2923574532633987,0.3545495763358322,0.7792511751183135,1149935,422929,0.37460634087325007,2.379917735586424,1217449,355415,0.45460735085160137,0.06301907077167415,3.5176991828098956,0.9669707940549266,0.017619386011240433,0.0006483428332270409,,,,,,,,,0.2688910166422526,422929,,,,,,,,,
+D_order1,D,salt1,fft,5,2,3,gated_product,0.3369852880639008,0.3883961879576229,0.8191156176528941,1217449,355415,1.7196420726675254,3.8303675590413384,1217449,355415,1.7264545666376496,,,,,,0.5220533274203073,0.4506855834671826,0.32851752595783085,1217449,0.5518861740054841,0.7125609149873865,1217449,0.22596677144368488,,,0.18020798168467977,0.14001642947049492,1217449,0.4150527808432428,0.1346623189397722,6291456,1421660,1.0,0.22599166666666667
+D_order1,D,salt1,fft,5,2,3,residual_delta,0.2638936487407193,0.3382020511848217,0.7140666204431241,393188,28,0.1095374558684127,0.9636068027746425,393188,28,0.21439108965615183,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,6,3,1,carrier,0.11837218080501932,0.2210460467064777,0.5596761128508992,3145728,0,7.381861577760441,4.027542953715896,3145728,0,12.366126064726728,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,6,3,1,gate_input,0.11805300104816988,0.2207874659268793,0.5591345482883711,3145728,0,7.377026331428713,4.0269559932981585,3145728,0,12.362336783208509,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,6,3,1,gate_value,0.28195907852538954,0.3367641961377622,0.7297899170074149,2879112,266616,1.4097132944856656,2.6406961305912535,2946249,199479,1.5931959607005393,0.5266914287702751,2.5542291781745528,0.8533580546476403,0.06910674426020408,0.03931037747130102,,,,,,,,,0.08475494384765625,266616,,,,,,,,,
+D_order1,D,salt1,fft,6,3,1,gated_product,0.34524057000206737,0.3797915149145565,0.7872261026801393,2946249,199479,9.783161630690433,4.367415527512718,2946249,199479,9.955299135690202,,,,,,0.699212423841705,0.5230085254621988,0.3368086427226429,2946249,0.6388176179539231,0.6819646842804696,2946249,0.06341266632080078,,,0.23061545598460992,0.16170386008385795,2946249,0.43996158793712814,0.14193693689148743,12582912,797916,1.0,0.063430078125
+D_order1,D,salt1,fft,6,3,1,residual_delta,0.1438866415442151,0.24879142858596143,0.5874692534137342,786432,0,0.009616255496983122,-1.751682091400272,786432,0,0.013289766289464409,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,7,3,2,carrier,0.2883116281786369,0.34357252763977425,0.7714419699867531,3145728,0,1.916896554941004,2.8493972522910864,3145728,0,4.286256454662757,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,7,3,2,gate_input,0.28837130976901115,0.3435472146378477,0.7715983876900243,3145728,0,1.910670104758177,2.8464908491648635,3145728,0,4.283212846058868,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,7,3,2,gate_value,0.48609456768956694,0.45596227598525885,0.876392187326475,2564019,581709,0.1972836021343661,1.1839579815680432,2580417,565311,0.20426846129226772,0.030492165546889986,4.287607858521205,0.9664916992187499,0.029701077208227036,0.0,,,,,,,,,0.18492031097412107,581709,,,,,,,,,
+D_order1,D,salt1,fft,7,3,2,gated_product,0.5148380780028705,0.47285246980176704,0.8967269335102148,2580417,565311,0.673273927744991,2.2312457945259316,2580417,565311,0.6804811676710354,,,,,,0.21366472384031612,0.15513250535166584,0.2893049967733191,2580417,0.6997792046522529,0.8515223356322261,2580417,0.17970752716064453,,,0.22443603377542248,0.1281711049830106,2580417,0.508648960704372,0.18090975710726534,12582912,2261244,1.0,0.17994635416666666
+D_order1,D,salt1,fft,7,3,2,residual_delta,0.23012331193103308,0.3057286574303334,0.6422704951164784,786432,0,0.005154996904885558,-2.2918280298384746,786432,0,0.008573897739069539,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,8,3,3,carrier,0.4705919076811872,0.4486337212016005,0.8571188914415911,3145728,0,1.820419647434004,2.8023273041144976,3145728,0,3.4034617520547545,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,8,3,3,gate_input,0.46954181972360937,0.4481006981086683,0.856841817899501,3145728,0,1.8133300420498397,2.798324563634755,3145728,0,3.3954503979782205,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,8,3,3,gate_value,0.5433873932717052,0.4890960533748048,0.9053817498163053,2910336,235392,0.47714005670262793,1.760332917257236,2916694,229034,0.5114533837766954,0.13083304372364124,3.0149557490556553,0.8841179049744898,0.11129387057557397,0.0,,,,,,,,,0.0748291015625,235392,,,,,,,,,
+D_order1,D,salt1,fft,8,3,3,gated_product,0.5646051248456307,0.500738054804417,0.9198381794627347,2916694,229034,1.4337477407924832,2.6761441888268234,2916694,229034,1.4837825025162772,,,,,,0.014152849740635643,0.006842888114175802,0.24255378903991942,2916694,0.7698482426438298,0.8295511068878876,2916694,0.07280794779459634,,,0.08733942719458793,0.048302266664986056,2916694,0.5230938440361962,0.19038468306146739,12582912,916136,1.0,0.07279114583333333
+D_order1,D,salt1,fft,8,3,3,residual_delta,0.3245484723913475,0.3701868672002405,0.774005359982653,786432,0,0.21608095168059632,0.9496223464222604,786432,0,0.24991054130654394,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,9,3,4,carrier,0.3972577029583245,0.40575622240003495,0.8341819536630358,3145728,0,1.6181643694472996,2.6978684451548176,3145728,0,3.5500595483317534,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,9,3,4,gate_input,0.39685345133977945,0.4057618620516758,0.8345668666877173,3145728,0,1.624845957752852,2.7015131580386926,3145728,0,3.5583752336585603,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,9,3,4,gate_value,0.523988725462917,0.47744485261573255,0.9053316235964806,2610077,535651,0.23617619301727377,1.3566197978742642,2610663,535065,0.24224229090040827,0.04502330045085509,2.9362692014658114,0.9453330526546555,0.054171815210459176,0.0,,,,,,,,,0.17027886708577472,535651,,,,,,,,,
+D_order1,D,salt1,fft,9,3,4,gated_product,0.5479025487631326,0.49076016308855336,0.9192181501510399,2610663,535065,0.7368928285111281,2.285850039177108,2610663,535065,0.7520648350160858,,,,,,0.031319902404089564,0.017779986184632883,0.255347832797006,2610663,0.7009154373822211,0.8425043346152654,2610663,0.17009258270263672,,,0.1415669972799758,0.07973357863118384,2610663,0.4692792836212525,0.17013439599642843,12582912,2140260,1.0,0.16995944010416666
+D_order1,D,salt1,fft,9,3,4,residual_delta,0.2590001179249547,0.3301869273461479,0.7210465378179864,786432,0,0.19026512054480016,0.8394436470754328,786432,0,0.2551689389372256,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,10,3,5,carrier,0.28257718615073224,0.34162365672244155,0.7912159669728531,3145728,0,1.8215593723082244,2.803373046252447,3145728,0,4.5197384539751395,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,10,3,5,gate_input,0.28288391260311907,0.34188483648303586,0.7918244876484886,3145728,0,1.8172318102784364,2.8011762139268095,3145728,0,4.50897898750823,,,,,,,,,,,,,,,,,,,,,,,,
+D_order1,D,salt1,fft,10,3,5,gate_value,0.4999225271871335,0.4646434913632285,0.907480758095804,2250983,894745,0.09875501828550415,0.8567962880100571,2329033,816695,0.11641937197107965,0.0141269067138491,3.1858963294203306,0.9790568449059311,0.017789256816007654,0.0,,,,,,,,,0.28443177541097003,894745,,,,,,,,,
+D_order1,D,salt1,fft,10,3,5,gated_product,0.5168347522223048,0.47474458036143896,0.9191281534096184,2329033,816695,0.41075147764342074,2.047916900684182,2329033,816695,0.4146254232570401,,,,,,0.07654891821713809,0.05198002930158857,0.27106804468259943,2329033,0.6276307849053315,0.8460281093893909,2329033,0.2596203486124674,,,0.2261683054181774,0.12822481915105344,2329033,0.4305796188084876,0.15489099053972302,12582912,3266780,1.0,0.2597952473958333
+D_order1,D,salt1,fft,10,3,5,residual_delta,0.20877910757497642,0.30525318799445633,0.6972180423551759,786432,0,0.08337479680633186,0.12515017233549933,786432,0,0.11348406668577934,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,5,2,3,carrier,0.15609176757215631,0.2584649137961378,0.6315598320219584,1572864,0,3.8181585064838517,4.054114691354383,1572864,0,10.210965699635947,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,6,3,1,carrier,0.08223315195434107,0.20161286567505785,0.4731293881660507,3145728,0,7.381861577760439,4.027542953715896,3145728,0,12.366126064726728,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,7,3,2,carrier,0.23541121459660644,0.3213406866781538,0.7007994196615013,3145728,0,1.9168965549410037,2.8493972522910864,3145728,0,4.286256454662757,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,8,3,3,carrier,0.4344153588574654,0.44303974672963153,0.7931401162303333,3145728,0,1.8204196474340035,2.802327304114497,3145728,0,3.4034617520547545,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,9,3,4,carrier,0.35563301752769433,0.3933859171117302,0.7660876265080225,3145728,0,1.6181643694472991,2.6978684451548176,3145728,0,3.5500595483317534,,,,,,,,,,,,,,,,,,,,,,,,
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+D_order1,D,salt1,dct,10,3,5,carrier,0.2518021375127043,0.3298393003902047,0.7256459734480745,3145728,0,1.821559372308224,2.803373046252447,3145728,0,4.5197384539751395,,,,,,,,,,,,,,,,,,,,,,,,
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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/D_order1/spectral_by_stage.csv

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/S/batch_pairing_manifest.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/S/source_provenance.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/alignment_source_equivalence.json

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gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/input_verification.json

@@ -0,0 +1,86 @@
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+      "official_top1": 77.794,
+      "permutation_manifest_sha256": "0219d0a289ae2e3c2bde90568af4b05d23ca629522f1b19b6a1d09473d87fb8b",
+      "status": "passed"
+    },
+    {
+      "arm": "SC",
+      "checkpoint_sha256": "918925152daae6a1039f91690ee9d634296b18a08be7800e8db4c2315ac175be",
+      "official_top1": 69.50800000000001,
+      "permutation_manifest_sha256": "0219d0a289ae2e3c2bde90568af4b05d23ca629522f1b19b6a1d09473d87fb8b",
+      "status": "passed"
+    }
+  ],
+  "data": {
+    "class_to_idx_sha256": "2312848f9b87eb0d5afd5c3cd09fc3191dadfb5948dc7912639683c2bc5e5f1f",
+    "data_root": "/tmp/gmnet_data/imagenet-1k",
+    "first_relative_path": "n01440764/ILSVRC2012_val_00000293.JPEG",
+    "frozen_subset": {
+      "class_histogram": {
+        "4": 904,
+        "5": 96
+      },
+      "path": "/nfs/ywang29/GmNet/e4_spectral_audit/protocol/subset_manifest.jsonl",
+      "rows": 4096,
+      "sha256": "52a3e937d8862b19d6352e60833a864aa5bea376436acd2290415cac58232adb"
+    },
+    "last_relative_path": "n15075141/ILSVRC2012_val_00049174.JPEG",
+    "sample_index_sha256": "5ed16b5cd3ebd64e90cd7a8380b4e19d4c0d82eedcca3489dfa29313ce88d6f6",
+    "sampled_content_policy": "first_sample_per_class_up_to_1000",
+    "sampled_content_samples": 1000,
+    "sampled_content_sha256": "d80a57419bc9396df7c01e29a2b17698c02c3a9a68dc4006a1fe87ef129ed166",
+    "status": "ready"
+  },
+  "frozen_subset": {
+    "class_histogram": {
+      "4": 904,
+      "5": 96
+    },
+    "path": "/nfs/ywang29/GmNet/e4_spectral_audit/protocol/subset_manifest.jsonl",
+    "rows": 4096,
+    "sha256": "52a3e937d8862b19d6352e60833a864aa5bea376436acd2290415cac58232adb"
+  },
+  "manifest_sha256": "8b32da7c33f6b578dd2b5359d727afe313462be0c0040212a2640755ad2fadf7",
+  "protocol_id": "gmnet-e4-imagenet-matched-spectral-v1",
+  "runner": {
+    "path": "/nfs/ywang29/GmNet/e4_spectral_audit/code/run_e4_imagenet_spectral_audit.py",
+    "sha256": "2b56465319e651716170fbaa58d43f14924d70f694b0a273ab63255c2697edfe"
+  },
+  "runtime": {
+    "cuda_devices": 8,
+    "python": "3.12.10",
+    "torch": "2.9.0+cu130",
+    "torchvision": "0.24.0+cu130"
+  },
+  "source_equivalence": {
+    "path": "/tmp/gmnet_alignment_equivalence_torch29.json",
+    "sha256": "1c1ca618f3a3ce95c7fa76b8174bc96667319b6463ece2c6ded4fadcaa32bd21",
+    "status": "passed"
+  },
+  "status": "passed"
+}

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/package_verification_after.log

@@ -0,0 +1 @@
+Deployment package verified: /nfs/ywang29/GmNet/depoly/e4_imagenet_spectral_20260718/e4_matched_spectral_5arms_seed0.yaml

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/package_verification_before.log

@@ -0,0 +1 @@
+Deployment package verified: /nfs/ywang29/GmNet/depoly/e4_imagenet_spectral_20260718/e4_matched_spectral_5arms_seed0.yaml

gmnet/spectral_audit/results/matched_5arm_seed0_20260718.partial.715/validation.json

@@ -0,0 +1,203 @@
+{
+  "checks": {
+    "cross.all_eight_tasks": {
+      "details": {
+        "valid_tasks": [
+          "B",
+          "C",
+          "D",
+          "S",
+          "SC"
+        ]
+      },
+      "message": "cross-task validation requires all eight valid tasks",
+      "passed": false
+    },
+    "manifest.code_provenance": {
+      "details": {
+        "evidence": {
+          "alignment_source_gmnet_sha256": {
+            "actual_sha256": "5291156084aaf72936d1f8896057e7e606f74ef05aed856df4940e4838dbe3ac",
+            "expected_sha256": "5291156084aaf72936d1f8896057e7e606f74ef05aed856df4940e4838dbe3ac",
+            "path": "/nfs/ywang29/GmNet/e4_spectral_audit/source_snapshots/alignment_4a62/gmnet/models/gmnet.py"
+          },
+          "alignment_source_snapshot_manifest": {
+            "actual_sha256": "f590f522f8e9181eaa26b1688ed5d59a0598b9d4188fcd10f14516d24b070b19",
+            "expected_sha256": "f590f522f8e9181eaa26b1688ed5d59a0598b9d4188fcd10f14516d24b070b19",
+            "path": "/nfs/ywang29/GmNet/e4_spectral_audit/source_snapshots/alignment_4a62/source_manifest.json"
+          },
+          "alignment_training_manifest": {
+            "actual_sha256": "fa1a6ba00c82619dbd51b9af61489a34326b635978d83ef948584e45f93cddbc",
+            "expected_sha256": "fa1a6ba00c82619dbd51b9af61489a34326b635978d83ef948584e45f93cddbc",
+            "path": "/nfs/ywang29/GmNet/journal_exp/configs/code_manifests/e4_alignment_20260716.json"
+          },
+          "arm_training_code_sha256": {
+            "B": "f6bd434a2ac86c772f2e23683431f3bb7ccb06f539558682a3bea6464bacebf9",
+            "C": "4a62bc48370247d882196cd9afc1a0f1629a5bbfd522c3f599ffaf0c1187641f",
+            "D": "f6bd434a2ac86c772f2e23683431f3bb7ccb06f539558682a3bea6464bacebf9",
+            "S": "4a62bc48370247d882196cd9afc1a0f1629a5bbfd522c3f599ffaf0c1187641f",
+            "SC": "f6bd434a2ac86c772f2e23683431f3bb7ccb06f539558682a3bea6464bacebf9"
+          },
+          "audit_runtime_gmnet_sha256": {
+            "actual_sha256": "8446a0ed7d6863f81e88929f3277d363c961372210279de7d0e770f636852f3c",
+            "expected_sha256": "8446a0ed7d6863f81e88929f3277d363c961372210279de7d0e770f636852f3c",
+            "path": "/nfs/ywang29/GmNet/journal_exp/gmnet/models/gmnet.py"
+          },
+          "audit_runtime_manifest": {
+            "actual_sha256": "e354339142ffc72e669234a9f297113a64e5294ce8ff37dd658e62f723f0b239",
+            "expected_sha256": "e354339142ffc72e669234a9f297113a64e5294ce8ff37dd658e62f723f0b239",
+            "path": "/nfs/ywang29/GmNet/journal_exp/configs/code_manifests/e4_mechanism_followup_20260717.json"
+          }
+        }
+      },
+      "message": "training/runtime code manifests or source hashes no longer match",
+      "passed": true
+    },
+    "manifest.independent_frozen_sha256": {
+      "details": {
+        "actual_sha256": "8b32da7c33f6b578dd2b5359d727afe313462be0c0040212a2640755ad2fadf7",
+        "expected_sha256": "8b32da7c33f6b578dd2b5359d727afe313462be0c0040212a2640755ad2fadf7"
+      },
+      "message": "input manifest bytes differ from the independently frozen validator constant",
+      "passed": true
+    },
+    "manifest.independent_training_code_boundaries": {
+      "message": "training code boundaries differ from hardcoded validator constants",
+      "passed": true
+    },
+    "manifest.protocol_file": {
+      "details": {
+        "expected_sha256": "b66f0aa2f620135584483fb0057fe6fec9a55b4b501b08fd26065034a892b4a2",
+        "path": "/nfs/ywang29/GmNet/e4_spectral_audit/protocol/IMAGENET_MATCHED_SPECTRAL_AUDIT_PROTOCOL.md"
+      },
+      "message": "protocol path/hash does not match the frozen input manifest",
+      "passed": true
+    },
+    "manifest.protocol_hash_format": {
+      "message": "input manifest protocol SHA256 must be lowercase hex",
+      "passed": true
+    },
+    "manifest.protocol_id": {
+      "message": "input manifest protocol_id must match the frozen protocol",
+      "passed": true
+    },
+    "manifest.runtime_contract": {
+      "details": {
+        "expected": {
+          "canonical_batch_size": 256,
+          "max_eval_samples": 50000,
+          "parallel_tasks": 8,
+          "spectral_samples": 4096,
+          "torch": "2.9.0+cu130",
+          "torchvision": "0.24.0+cu130"
+        }
+      },
+      "message": "input manifest runtime contract changed",
+      "passed": true
+    },
+    "manifest.task_contract": {
+      "message": "input manifest must register five canonical arms and three order probes",
+      "passed": true
+    },
+    "root.source_equivalence": {
+      "details": {
+        "sha256": "1c1ca618f3a3ce95c7fa76b8174bc96667319b6463ece2c6ded4fadcaa32bd21"
+      },
+      "message": "formal torch-2.9 old/new source equivalence did not pass exactly",
+      "passed": true
+    },
+    "root.task_directories": {
+      "details": {
+        "missing": []
+      },
+      "message": "one or more required task directories are absent",
+      "passed": true
+    },
+    "task.B.artifacts": {
+      "message": "passed",
+      "passed": true
+    },
+    "task.B.official_float_drift": {
+      "details": {
+        "confidence_max_abs": 0.0,
+        "nll_max_abs": 0.0
+      },
+      "message": "recorded; discrete replay is exact",
+      "passed": true
+    },
+    "task.B_order1.artifacts": {
+      "message": "ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+      "passed": false
+    },
+    "task.C.artifacts": {
+      "message": "passed",
+      "passed": true
+    },
+    "task.C.official_float_drift": {
+      "details": {
+        "confidence_max_abs": 0.0,
+        "nll_max_abs": 0.0
+      },
+      "message": "recorded; discrete replay is exact",
+      "passed": true
+    },
+    "task.D.artifacts": {
+      "message": "passed",
+      "passed": true
+    },
+    "task.D.official_float_drift": {
+      "details": {
+        "confidence_max_abs": 0.0,
+        "nll_max_abs": 0.0
+      },
+      "message": "recorded; discrete replay is exact",
+      "passed": true
+    },
+    "task.D_order1.artifacts": {
+      "message": "ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+      "passed": false
+    },
+    "task.D_order2.artifacts": {
+      "message": "ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+      "passed": false
+    },
+    "task.S.artifacts": {
+      "message": "passed",
+      "passed": true
+    },
+    "task.S.official_float_drift": {
+      "details": {
+        "confidence_max_abs": 0.0,
+        "nll_max_abs": 0.0
+      },
+      "message": "recorded; discrete replay is exact",
+      "passed": true
+    },
+    "task.SC.artifacts": {
+      "message": "passed",
+      "passed": true
+    },
+    "task.SC.official_float_drift": {
+      "details": {
+        "confidence_max_abs": 0.0,
+        "nll_max_abs": 0.0
+      },
+      "message": "recorded; discrete replay is exact",
+      "passed": true
+    }
+  },
+  "errors": [
+    "task.D_order1.artifacts: ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+    "task.D_order2.artifacts: ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+    "task.B_order1.artifacts: ValueError: task protocol.json differs from the frozen protocol binding or numerical constants",
+    "cross.all_eight_tasks: cross-task validation requires all eight valid tasks"
+  ],
+  "formal_complete": false,
+  "input_manifest": "/nfs/ywang29/GmNet/e4_spectral_audit/input_manifest.json",
+  "input_manifest_sha256": "8b32da7c33f6b578dd2b5359d727afe313462be0c0040212a2640755ad2fadf7",
+  "protocol_id": "gmnet-e4-imagenet-matched-spectral-v1",
+  "schema_version": 1,
+  "status": "failed",
+  "validator_sha256": "9027155739ccbeba1150d5bdaa6d94930d8169ed4d4edb5c5fe2af2bd655b5e5",
+  "writes_complete_marker": false
+}

gmnet/spectral_audit/source_snapshots/alignment_4a62/gmnet/models/gmnet.py

@@ -0,0 +1,802 @@
+"""GmNet model definitions.
+
+This module keeps the published GmNet topology while avoiding the legacy timm
+model registry.  The default ``relu6_self`` gate implements the paper's
+``ReLU6(x) * x`` operation.  Alternative gates are intentionally parameterized
+independently from the stem activation so activation/gating ablations do not
+silently change the rest of the network.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import math
+import struct
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Final
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+SUPPORTED_GATE_TYPES: Final[tuple[str, ...]] = (
+    "relu6_self",
+    "relu6_only",
+    "relu_self",
+    "gelu_self",
+    "identity",
+    "no_gate",
+    "smooth_clipped_self",
+)
+SUPPORTED_GATE_INTERVENTIONS: Final[tuple[str, ...]] = (
+    "baseline",
+    "stop_gradient",
+    "channel_derangement",
+)
+GATE_INTERVENTION_SEED_STRIDE: Final[int] = 10_007
+_MAX_GATE_INTERVENTION_SEED: Final[int] = (1 << 63) - 1
+
+
+@dataclass(frozen=True)
+class GmNetConfig:
+    """Architecture settings reported in the GmNet paper."""
+
+    base_dim: int
+    depths: tuple[int, int, int, int]
+    mlp_ratios: tuple[int, int, int, int]
+
+    @property
+    def embed_dims(self) -> tuple[int, int, int, int]:
+        return tuple(self.base_dim * (2**index) for index in range(4))
+
+
+GMNET_CONFIGS: Final[dict[str, GmNetConfig]] = {
+    "s1": GmNetConfig(40, (2, 2, 10, 2), (3, 3, 3, 2)),
+    # The appendix prints (3, 3, 3, 2) for S2, which constructs a 5.27M
+    # model. The released experiment source uses (4, 4, 3, 3), matching the
+    # reported 6.2M parameter count, so that reproducible setting is used here.
+    "s2": GmNetConfig(48, (2, 2, 8, 3), (4, 4, 3, 3)),
+    "s3": GmNetConfig(48, (3, 3, 8, 3), (4, 4, 4, 4)),
+    "s4": GmNetConfig(68, (3, 3, 11, 3), (4, 4, 4, 4)),
+}
+
+
+class ConvBN(nn.Sequential):
+    """Convolution followed by optional batch normalization.
+
+    Biases are retained to match the released GmNet implementation and its
+    reported parameter counts, including for convolutions followed by BN.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 1,
+        stride: int = 1,
+        padding: int = 0,
+        dilation: int = 1,
+        groups: int = 1,
+        with_bn: bool = True,
+    ) -> None:
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        # Compatibility attributes used by the original CoreML conversion tool.
+        self.in_planes = in_channels
+        self.out_planes = out_channels
+        self.add_module(
+            "conv",
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride,
+                padding,
+                dilation,
+                groups,
+                bias=True,
+            ),
+        )
+        if with_bn:
+            self.add_module("bn", nn.BatchNorm2d(out_channels))
+
+
+class DropPath(nn.Module):
+    """Per-sample stochastic depth without a timm dependency."""
+
+    def __init__(self, drop_prob: float = 0.0) -> None:
+        super().__init__()
+        if not 0.0 <= drop_prob < 1.0:
+            raise ValueError(f"drop_prob must be in [0, 1), got {drop_prob}")
+        self.drop_prob = float(drop_prob)
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.drop_prob == 0.0 or not self.training:
+            return x
+        keep_prob = 1.0 - self.drop_prob
+        mask_shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+        mask = x.new_empty(mask_shape).bernoulli_(keep_prob)
+        return x * mask / keep_prob
+
+    def extra_repr(self) -> str:
+        return f"drop_prob={self.drop_prob:g}"
+
+
+class NoGate(nn.Module):
+    """Pass features through without activation or multiplication."""
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x
+
+
+def _normalize_gate_intervention(gate_intervention: str) -> str:
+    if not isinstance(gate_intervention, str):
+        raise TypeError("gate_intervention must be a string")
+    normalized = gate_intervention.lower().replace("-", "_")
+    if normalized not in SUPPORTED_GATE_INTERVENTIONS:
+        choices = ", ".join(SUPPORTED_GATE_INTERVENTIONS)
+        raise ValueError(
+            f"unknown gate_intervention {gate_intervention!r}; expected one of: "
+            f"{choices}"
+        )
+    return normalized
+
+
+def _validate_gate_intervention_seed(seed: int) -> int:
+    if isinstance(seed, bool) or not isinstance(seed, int):
+        raise TypeError("gate_intervention_seed must be an integer")
+    if not 0 <= seed <= _MAX_GATE_INTERVENTION_SEED:
+        raise ValueError(
+            "gate_intervention_seed must be in "
+            f"[0, {_MAX_GATE_INTERVENTION_SEED}], got {seed}"
+        )
+    return seed
+
+
+def _channel_derangement(channels: int, seed: int) -> Tensor:
+    if channels < 2:
+        raise ValueError(
+            "channel_derangement requires at least two expanded channels, "
+            f"got {channels}"
+        )
+    generator = torch.Generator(device="cpu")
+    generator.manual_seed(seed)
+    cycle = torch.randperm(channels, generator=generator)
+    permutation = torch.empty_like(cycle)
+    permutation[cycle] = cycle.roll(-1)
+    return permutation
+
+
+class _GateInterventionMixin:
+    """Select the tensor used by a multiplicative gate branch."""
+
+    gate_intervention: str
+    gate_intervention_seed: int
+    gate_permutation: Tensor | None
+
+    def _configure_gate_intervention(
+        self,
+        channels: int,
+        gate_intervention: str,
+        gate_intervention_seed: int,
+    ) -> None:
+        intervention = _normalize_gate_intervention(gate_intervention)
+        seed = _validate_gate_intervention_seed(gate_intervention_seed)
+        permutation = (
+            _channel_derangement(channels, seed)
+            if intervention == "channel_derangement"
+            else None
+        )
+        self.gate_intervention = intervention
+        self.gate_intervention_seed = seed
+        self._gate_channels = channels
+        self.register_buffer("gate_permutation", permutation, persistent=False)
+
+    def gate_input(self, x: Tensor) -> Tensor:
+        """Return the input seen by the gate branch."""
+
+        if self.gate_intervention == "baseline":
+            return x
+        if self.gate_intervention == "stop_gradient":
+            return x.detach()
+        if x.ndim < 2 or x.shape[1] != self._gate_channels:
+            raise ValueError(
+                "channel_derangement expected channel dimension "
+                f"{self._gate_channels}, got shape {tuple(x.shape)}"
+            )
+        assert self.gate_permutation is not None
+        return x.index_select(1, self.gate_permutation)
+
+
+class SelfGate(_GateInterventionMixin, nn.Module):
+    """Apply ``activation(x) * x`` using a shared gate/feature tensor."""
+
+    def __init__(
+        self,
+        activation: nn.Module,
+        name: str,
+        *,
+        channels: int,
+        gate_intervention: str = "baseline",
+        gate_intervention_seed: int = 0,
+    ) -> None:
+        super().__init__()
+        self.activation = activation
+        self.name = name
+        self._configure_gate_intervention(
+            channels, gate_intervention, gate_intervention_seed
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.activation(self.gate_input(x)) * x
+
+    def extra_repr(self) -> str:
+        return f"name={self.name}, gate_intervention={self.gate_intervention}"
+
+
+class ActivationOnlyGate(nn.Module):
+    """Apply an activation without multiplicative self-gating."""
+
+    def __init__(self, activation: nn.Module, name: str) -> None:
+        super().__init__()
+        self.activation = activation
+        self.name = name
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.activation(x)
+
+    def extra_repr(self) -> str:
+        return f"name={self.name}"
+
+
+class SmoothClippedSelfGate(_GateInterventionMixin, nn.Module):
+    r"""A static differentiable approximation to ReLU6 self-gating.
+
+    For each expanded feature channel, the gate is
+
+    .. math::
+
+       g(x; \tau) = \operatorname{softplus}(\beta x) / \beta
+                    - \operatorname{softplus}(\beta(x-\tau)) / \beta,
+
+    followed by ``x * g(x; tau)``.  The positive threshold ``tau`` is either
+    trainable or held fixed, and is always fixed across samples and spatial
+    locations. This is therefore a static gate, not an input-conditioned
+    controller.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        init_clip: float = 6.0,
+        beta: float = 10.0,
+        per_channel: bool = True,
+        trainable: bool = True,
+        min_clip: float = 1e-4,
+        gate_intervention: str = "baseline",
+        gate_intervention_seed: int = 0,
+    ) -> None:
+        super().__init__()
+        if channels <= 0:
+            raise ValueError(f"channels must be positive, got {channels}")
+        if init_clip <= min_clip:
+            raise ValueError(
+                f"init_clip must be greater than min_clip ({min_clip}), got {init_clip}"
+            )
+        if beta <= 0:
+            raise ValueError(f"beta must be positive, got {beta}")
+
+        parameter_channels = channels if per_channel else 1
+        raw_init = math.log(math.expm1(init_clip - min_clip))
+        clip_shape = (1, parameter_channels, 1, 1)
+        if trainable:
+            self.raw_clip = nn.Parameter(torch.full(clip_shape, raw_init))
+            self.register_buffer("fixed_clip", None)
+        else:
+            self.register_parameter("raw_clip", None)
+            self.register_buffer("fixed_clip", torch.full(clip_shape, init_clip))
+        self.channels = channels
+        self.beta = float(beta)
+        self.per_channel = per_channel
+        self.trainable = trainable
+        self.min_clip = float(min_clip)
+        self._configure_gate_intervention(
+            channels, gate_intervention, gate_intervention_seed
+        )
+
+    @property
+    def clip_value(self) -> Tensor:
+        if self.raw_clip is None:
+            assert self.fixed_clip is not None
+            return self.fixed_clip
+        return F.softplus(self.raw_clip) + self.min_clip
+
+    def gate_value(self, x: Tensor) -> Tensor:
+        if x.ndim != 4:
+            raise ValueError(f"expected a BCHW tensor, got shape {tuple(x.shape)}")
+        if x.shape[1] != self.channels:
+            raise ValueError(f"expected {self.channels} channels, got {x.shape[1]}")
+        clip = self.clip_value
+        lower = F.softplus(x, beta=self.beta)
+        upper = F.softplus(x - clip, beta=self.beta)
+        return lower - upper
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x * self.gate_value(self.gate_input(x))
+
+    def extra_repr(self) -> str:
+        return (
+            f"channels={self.channels}, beta={self.beta:g}, "
+            f"per_channel={self.per_channel}, trainable={self.trainable}, "
+            f"gate_intervention={self.gate_intervention}"
+        )
+
+
+def build_gate(
+    gate_type: str,
+    channels: int,
+    *,
+    smooth_clip_init: float = 6.0,
+    smooth_clip_beta: float = 10.0,
+    smooth_clip_per_channel: bool = True,
+    smooth_clip_trainable: bool = True,
+    gate_intervention: str = "baseline",
+    gate_intervention_seed: int = 0,
+) -> nn.Module:
+    """Construct a GmNet gate.
+
+    ``identity`` means an identity activation inside the self-gate and thus
+    computes ``x * x``.  ``no_gate`` is the distinct pass-through ablation.
+    """
+
+    normalized = gate_type.lower().replace("-", "_")
+    normalized = {
+        "relu6": "relu6_self",
+        "activation_only": "relu6_only",
+        "relu6_activation_only": "relu6_only",
+        "relu": "relu_self",
+        "gelu": "gelu_self",
+        "smooth_clipped_static": "smooth_clipped_self",
+    }.get(normalized, normalized)
+    intervention = _normalize_gate_intervention(gate_intervention)
+    if normalized == "relu6_self":
+        return SelfGate(
+            nn.ReLU6(inplace=False),
+            normalized,
+            channels=channels,
+            gate_intervention=intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+    if normalized == "relu6_only":
+        if intervention != "baseline":
+            raise ValueError(
+                "gate_intervention requires a multiplicative self-gate; "
+                "relu6_only is activation-only"
+            )
+        return ActivationOnlyGate(nn.ReLU6(inplace=False), normalized)
+    if normalized == "relu_self":
+        return SelfGate(
+            nn.ReLU(inplace=False),
+            normalized,
+            channels=channels,
+            gate_intervention=intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+    if normalized == "gelu_self":
+        return SelfGate(
+            nn.GELU(),
+            normalized,
+            channels=channels,
+            gate_intervention=intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+    if normalized == "identity":
+        return SelfGate(
+            nn.Identity(),
+            normalized,
+            channels=channels,
+            gate_intervention=intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+    if normalized == "no_gate":
+        if intervention != "baseline":
+            raise ValueError(
+                "gate_intervention requires a multiplicative self-gate; "
+                "no_gate has no gate branch"
+            )
+        return NoGate()
+    if normalized in {"smooth_clipped", "smooth_clipped_self"}:
+        return SmoothClippedSelfGate(
+            channels,
+            init_clip=smooth_clip_init,
+            beta=smooth_clip_beta,
+            per_channel=smooth_clip_per_channel,
+            trainable=smooth_clip_trainable,
+            gate_intervention=intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+    choices = ", ".join(SUPPORTED_GATE_TYPES)
+    raise ValueError(f"unknown gate_type {gate_type!r}; expected one of: {choices}")
+
+
+class GmNetBlock(nn.Module):
+    """The residual block used by all four published GmNet variants."""
+
+    def __init__(
+        self,
+        dim: int,
+        mlp_ratio: int = 3,
+        kernel_size: int = 7,
+        gate_type: str = "relu6_self",
+        f12_bn: bool = False,
+        projection_bn: bool = True,
+        second_dw_bn: bool = False,
+        drop_path: float = 0.0,
+        layer_scale: float = 1e-6,
+        smooth_clip_init: float = 6.0,
+        smooth_clip_beta: float = 10.0,
+        smooth_clip_per_channel: bool = True,
+        smooth_clip_trainable: bool = True,
+        gate_intervention: str = "baseline",
+        gate_intervention_seed: int = 0,
+    ) -> None:
+        super().__init__()
+        if dim <= 0 or mlp_ratio <= 0:
+            raise ValueError("dim and mlp_ratio must be positive")
+        if kernel_size <= 0 or kernel_size % 2 == 0:
+            raise ValueError("kernel_size must be a positive odd integer")
+
+        hidden_dim = dim * mlp_ratio
+        padding = (kernel_size - 1) // 2
+        self.dwconv = ConvBN(
+            dim,
+            dim,
+            kernel_size,
+            padding=padding,
+            groups=dim,
+            with_bn=True,
+        )
+        self.f1 = ConvBN(dim, hidden_dim, 1, with_bn=f12_bn)
+        self.gate = build_gate(
+            gate_type,
+            hidden_dim,
+            smooth_clip_init=smooth_clip_init,
+            smooth_clip_beta=smooth_clip_beta,
+            smooth_clip_per_channel=smooth_clip_per_channel,
+            smooth_clip_trainable=smooth_clip_trainable,
+            gate_intervention=gate_intervention,
+            gate_intervention_seed=gate_intervention_seed,
+        )
+        self.g = ConvBN(hidden_dim, dim, 1, with_bn=projection_bn)
+        self.dwconv2 = ConvBN(
+            dim,
+            dim,
+            kernel_size,
+            padding=padding,
+            groups=dim,
+            with_bn=second_dw_bn,
+        )
+        if layer_scale > 0:
+            self.gamma = nn.Parameter(layer_scale * torch.ones(dim))
+        else:
+            self.register_parameter("gamma", None)
+        self.drop_path = DropPath(drop_path)
+        self.gate_type = gate_type
+        self.gate_intervention = _normalize_gate_intervention(gate_intervention)
+        self.gate_intervention_seed = _validate_gate_intervention_seed(
+            gate_intervention_seed
+        )
+        self.gate_channels = hidden_dim
+
+    @property
+    def gate_permutation(self) -> Tensor | None:
+        value = getattr(self.gate, "gate_permutation", None)
+        return value if isinstance(value, Tensor) else None
+
+    def forward(self, x: Tensor) -> Tensor:
+        residual = x
+        x = self.dwconv(x)
+        x = self.f1(x)
+        x = self.gate(x)
+        x = self.g(x)
+        x = self.dwconv2(x)
+        if self.gamma is not None:
+            x = x * self.gamma.view(1, -1, 1, 1)
+        return residual + self.drop_path(x)
+
+
+def _stage_gate_types(gate_type: str | Sequence[str], stages: int) -> tuple[str, ...]:
+    if isinstance(gate_type, str):
+        return (gate_type,) * stages
+    values = tuple(gate_type)
+    if len(values) != stages:
+        raise ValueError(f"gate_type must contain {stages} values, got {len(values)}")
+    return values
+
+
+def _block_gate_intervention_seed(base_seed: int, block_index: int) -> int:
+    seed = base_seed + block_index * GATE_INTERVENTION_SEED_STRIDE
+    if seed > _MAX_GATE_INTERVENTION_SEED:
+        raise ValueError(
+            "derived gate intervention seed exceeds the supported maximum: "
+            f"base={base_seed}, block_index={block_index}, derived={seed}"
+        )
+    return seed
+
+
+def _stem_activation(name: str) -> nn.Module:
+    normalized = name.lower().replace("-", "_")
+    if normalized == "relu6":
+        return nn.ReLU6(inplace=False)
+    if normalized == "relu":
+        return nn.ReLU(inplace=False)
+    if normalized == "gelu":
+        return nn.GELU()
+    if normalized in {"identity", "none"}:
+        return nn.Identity()
+    raise ValueError(f"unsupported stem_activation: {name!r}")
+
+
+class GmNet(nn.Module):
+    """Hierarchical GmNet image classifier."""
+
+    def __init__(
+        self,
+        embed_dims: Sequence[int] = (48, 96, 192, 384),
+        depths: Sequence[int] = (3, 3, 8, 3),
+        mlp_ratios: Sequence[int] = (4, 4, 4, 4),
+        *,
+        num_classes: int = 1000,
+        in_channels: int = 3,
+        stem_dim: int = 32,
+        stem_activation: str = "relu6",
+        gate_type: str | Sequence[str] = "relu6_self",
+        kernel_size: int = 7,
+        drop_path_rate: float = 0.0,
+        layer_scale: float = 1e-6,
+        f12_bn: bool = False,
+        projection_bn: bool = True,
+        second_dw_bn: bool = False,
+        smooth_clip_init: float = 6.0,
+        smooth_clip_beta: float = 10.0,
+        smooth_clip_per_channel: bool = True,
+        smooth_clip_trainable: bool = True,
+        gate_intervention: str = "baseline",
+        gate_intervention_seed: int = 0,
+    ) -> None:
+        super().__init__()
+        embed_dims = tuple(int(value) for value in embed_dims)
+        depths = tuple(int(value) for value in depths)
+        mlp_ratios = tuple(int(value) for value in mlp_ratios)
+        stage_count = len(embed_dims)
+        if (
+            stage_count == 0
+            or len(depths) != stage_count
+            or len(mlp_ratios) != stage_count
+        ):
+            raise ValueError(
+                "embed_dims, depths, and mlp_ratios must have equal non-zero length"
+            )
+        if any(value <= 0 for value in (*embed_dims, *depths, *mlp_ratios)):
+            raise ValueError("embed_dims, depths, and mlp_ratios must be positive")
+        if num_classes < 0:
+            raise ValueError("num_classes cannot be negative")
+        if not 0.0 <= drop_path_rate < 1.0:
+            raise ValueError("drop_path_rate must be in [0, 1)")
+
+        self.num_classes = num_classes
+        self.num_features = embed_dims[-1]
+        self.embed_dims = embed_dims
+        self.depths = depths
+        self.mlp_ratios = mlp_ratios
+        self.gate_types = _stage_gate_types(gate_type, stage_count)
+        self.gate_intervention = _normalize_gate_intervention(gate_intervention)
+        self.gate_intervention_seed = _validate_gate_intervention_seed(
+            gate_intervention_seed
+        )
+        if depths:
+            _block_gate_intervention_seed(self.gate_intervention_seed, sum(depths) - 1)
+
+        self.stem = nn.Sequential(
+            ConvBN(in_channels, stem_dim, kernel_size=3, stride=2, padding=1),
+            _stem_activation(stem_activation),
+        )
+
+        drop_rates = torch.linspace(0, drop_path_rate, sum(depths)).tolist()
+        self.stages = nn.ModuleList()
+        block_index = 0
+        current_dim = stem_dim
+        for stage_index, (dim, depth, ratio) in enumerate(
+            zip(embed_dims, depths, mlp_ratios, strict=True)
+        ):
+            downsample = nn.Sequential(
+                ConvBN(current_dim, dim, kernel_size=3, stride=2, padding=1),
+                nn.Identity(),
+            )
+            blocks = [
+                GmNetBlock(
+                    dim,
+                    mlp_ratio=ratio,
+                    kernel_size=kernel_size,
+                    gate_type=self.gate_types[stage_index],
+                    f12_bn=f12_bn,
+                    projection_bn=projection_bn,
+                    second_dw_bn=second_dw_bn,
+                    drop_path=drop_rates[block_index + offset],
+                    layer_scale=layer_scale,
+                    smooth_clip_init=smooth_clip_init,
+                    smooth_clip_beta=smooth_clip_beta,
+                    smooth_clip_per_channel=smooth_clip_per_channel,
+                    smooth_clip_trainable=smooth_clip_trainable,
+                    gate_intervention=self.gate_intervention,
+                    gate_intervention_seed=_block_gate_intervention_seed(
+                        self.gate_intervention_seed, block_index + offset
+                    ),
+                )
+                for offset in range(depth)
+            ]
+            self.stages.append(nn.Sequential(downsample, *blocks))
+            block_index += depth
+            current_dim = dim
+
+        self.norm = nn.BatchNorm2d(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+        self.head = (
+            nn.Linear(self.num_features, num_classes)
+            if num_classes > 0
+            else nn.Identity()
+        )
+        self.apply(self._init_weights)
+
+    @staticmethod
+    def _init_weights(module: nn.Module) -> None:
+        # The released implementation leaves Conv2d at its PyTorch default
+        # initialization and applies truncated normal initialization to the head.
+        if isinstance(module, nn.Linear):
+            nn.init.trunc_normal_(module.weight, std=0.02)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.BatchNorm2d):
+            nn.init.ones_(module.weight)
+            nn.init.zeros_(module.bias)
+
+    def forward_features(self, x: Tensor) -> Tensor:
+        if x.ndim != 4:
+            raise ValueError(f"expected a BCHW tensor, got shape {tuple(x.shape)}")
+        x = self.stem(x)
+        for stage in self.stages:
+            x = stage(x)
+        return self.norm(x)
+
+    def forward_head(self, x: Tensor) -> Tensor:
+        x = self.avgpool(x).flatten(1)
+        return self.head(x)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.forward_head(self.forward_features(x))
+
+    def reset_classifier(self, num_classes: int) -> None:
+        if num_classes < 0:
+            raise ValueError("num_classes cannot be negative")
+        self.num_classes = num_classes
+        self.head = (
+            nn.Linear(self.num_features, num_classes)
+            if num_classes > 0
+            else nn.Identity()
+        )
+        if isinstance(self.head, nn.Linear):
+            self._init_weights(self.head)
+
+    def gate_intervention_metadata(self) -> list[dict[str, object]]:
+        """Return canonical per-block metadata for checkpoint/eval audits.
+
+        Permutation hashes use contiguous little-endian signed int64 values.
+        This representation is independent of the tensor's source dtype and
+        host byte order.
+        """
+
+        metadata: list[dict[str, object]] = []
+        global_block_index = 0
+        for stage_index, stage in enumerate(self.stages):
+            for stage_block_index, module in enumerate(stage[1:]):
+                if not isinstance(module, GmNetBlock):
+                    continue
+                permutation = module.gate_permutation
+                if permutation is None:
+                    permutation_sha256 = None
+                    fixed_points = None
+                    is_bijection = None
+                else:
+                    values = [int(value) for value in permutation.detach().cpu()]
+                    payload = struct.pack(f"<{len(values)}q", *values)
+                    permutation_sha256 = hashlib.sha256(payload).hexdigest()
+                    fixed_points = sum(
+                        value == index for index, value in enumerate(values)
+                    )
+                    is_bijection = sorted(values) == list(range(len(values)))
+                metadata.append(
+                    {
+                        "global_block_index": global_block_index,
+                        "stage_index": stage_index,
+                        "stage_block_index": stage_block_index,
+                        "mode": module.gate_intervention,
+                        "seed": module.gate_intervention_seed,
+                        "channels": module.gate_channels,
+                        "permutation_sha256": permutation_sha256,
+                        "permutation_hash_encoding": (
+                            "little_endian_int64_c_order"
+                            if permutation is not None
+                            else None
+                        ),
+                        "fixed_points": fixed_points,
+                        "is_bijection": is_bijection,
+                    }
+                )
+                global_block_index += 1
+        return metadata
+
+
+def create_gmnet(
+    variant: str,
+    *,
+    pretrained: bool = False,
+    **kwargs: object,
+) -> GmNet:
+    """Create a paper GmNet variant without a framework-specific registry."""
+
+    normalized = variant.lower().replace("gmnet_", "").replace("-", "")
+    if normalized not in GMNET_CONFIGS:
+        choices = ", ".join(f"gmnet_{name}" for name in GMNET_CONFIGS)
+        raise ValueError(f"unknown variant {variant!r}; expected one of: {choices}")
+    if pretrained:
+        raise ValueError(
+            "pretrained=True is not supported because no versioned checkpoint "
+            "URL is bundled; instantiate the model and load a state_dict explicitly"
+        )
+    config = GMNET_CONFIGS[normalized]
+    return GmNet(
+        embed_dims=config.embed_dims,
+        depths=config.depths,
+        mlp_ratios=config.mlp_ratios,
+        **kwargs,
+    )
+
+
+def gmnet_s1(*, pretrained: bool = False, **kwargs: object) -> GmNet:
+    return create_gmnet("s1", pretrained=pretrained, **kwargs)
+
+
+def gmnet_s2(*, pretrained: bool = False, **kwargs: object) -> GmNet:
+    return create_gmnet("s2", pretrained=pretrained, **kwargs)
+
+
+def gmnet_s3(*, pretrained: bool = False, **kwargs: object) -> GmNet:
+    return create_gmnet("s3", pretrained=pretrained, **kwargs)
+
+
+def gmnet_s4(*, pretrained: bool = False, **kwargs: object) -> GmNet:
+    return create_gmnet("s4", pretrained=pretrained, **kwargs)
+
+
+__all__ = [
+    "GATE_INTERVENTION_SEED_STRIDE",
+    "GMNET_CONFIGS",
+    "SUPPORTED_GATE_INTERVENTIONS",
+    "SUPPORTED_GATE_TYPES",
+    "ActivationOnlyGate",
+    "ConvBN",
+    "DropPath",
+    "GmNet",
+    "GmNetBlock",
+    "GmNetConfig",
+    "SmoothClippedSelfGate",
+    "build_gate",
+    "create_gmnet",
+    "gmnet_s1",
+    "gmnet_s2",
+    "gmnet_s3",
+    "gmnet_s4",
+]