exp17 building-latent checkpoints (ad_*, cx_*), seed 20261105

experiment/exp/exp17-building-latent/runs/exp17/ad_RCF/seed20261105/best.pt

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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "ad_RCF",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "RCF"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "auto",
+      "dim": 32,
+      "init_std": 0.1,
+      "prior": 0.001,
+      "drop": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "fit_steps": 80,
+      "fit_steps_select": 25,
+      "fit_lr": 0.05
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 8336,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
+    "steps_passes": 15,
+    "steps_clip": [
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+      20000
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+    "batch_size": 32,
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+    "weight_decay": 1e-05,
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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "ad_RCW",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "RCW"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "auto",
+      "dim": 32,
+      "init_std": 0.1,
+      "prior": 0.001,
+      "drop": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "fit_steps": 80,
+      "fit_steps_select": 25,
+      "fit_lr": 0.05
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 18122,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
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experiment/exp/exp17-building-latent/runs/exp17/ad_RM/seed20261105/latents.npz

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experiment/exp/exp17-building-latent/runs/exp17/ad_RM/seed20261105/provenance.json

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+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "auto",
+      "dim": 32,
+      "init_std": 0.1,
+      "prior": 0.001,
+      "drop": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "fit_steps": 80,
+      "fit_steps_select": 25,
+      "fit_lr": 0.05
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 4947,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
+    "steps_passes": 15,
+    "steps_clip": [
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+      20000
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+    "batch_size": 32,
+    "batch_sampling": "mixed",
+    "learning_rate": 0.0001,
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+    "warmup_steps": 200,
+    "weight_decay": 1e-05,
+    "max_grad_norm": 1,
+    "normalization": "per_case",
+    "response_weights": [
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+      1,
+      1
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+    "train_monitor_cases": 2000,
+    "eval_batch": 64,
+    "checkpoint_selection": "validation (three response terms on non-failed cases) + ds_weight x validation cross-entropy",
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+      "num_structure_types": 4,
+      "encoder_conditioning": "none",
+      "decoder_type": "xattn",
+      "decoder_structure": "film",
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+      "local_snippet": false,
+      "snippet_context": 30,
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+      "ground_sign": -1,
+      "amplitude_factorization": true,
+      "concat_query_dim": 32,
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+      "fno_width": 64,
+      "fno_layers": 4,
+      "fno_mix_modes": 512,
+      "fno_token_channels": 48,
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+      "decoder_split": "separate",
+      "acceleration_decoder_type": "xattn",
+      "xa_width": 128,
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+      "xa_layers": 4,
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+      "xa_snippet_context": 25,
+      "encoder_type": "fno",
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+      "spectral_context": 30,
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+      "pfa_weight": 0.1,
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experiment/exp/exp17-building-latent/runs/exp17/ad_URM/seed20261105/latents.npz

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experiment/exp/exp17-building-latent/runs/exp17/ad_URM/seed20261105/provenance.json

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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "ad_URM",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "URM"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "auto",
+      "dim": 32,
+      "init_std": 0.1,
+      "prior": 0.001,
+      "drop": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "fit_steps": 80,
+      "fit_steps_select": 25,
+      "fit_lr": 0.05
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 3000,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
+    "steps_passes": 15,
+    "steps_clip": [
+      3000,
+      20000
+    ],
+    "batch_size": 32,
+    "batch_sampling": "mixed",
+    "learning_rate": 0.0001,
+    "minimum_learning_rate": 1e-06,
+    "warmup_steps": 200,
+    "weight_decay": 1e-05,
+    "max_grad_norm": 1,
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experiment/exp/exp17-building-latent/runs/exp17/cx_RCF/seed20261105/latents.npz

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experiment/exp/exp17-building-latent/runs/exp17/cx_RCF/seed20261105/provenance.json

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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "cx_RCF",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "RCF"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "context",
+      "dim": 32,
+      "prior": 0.0001,
+      "p_zero": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "context_width": 128,
+      "context_layers": 2,
+      "context_heads": 4,
+      "context_dropout": 0.1,
+      "fit_steps": 0,
+      "fit_steps_select": 0
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 8336,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
+    "steps_passes": 15,
+    "steps_clip": [
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+      20000
+    ],
+    "batch_size": 32,
+    "batch_sampling": "mixed",
+    "learning_rate": 0.0001,
+    "minimum_learning_rate": 1e-06,
+    "warmup_steps": 200,
+    "weight_decay": 1e-05,
+    "max_grad_norm": 1,
+    "normalization": "per_case",
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+    "train_monitor_cases": 2000,
+    "eval_batch": 64,
+    "checkpoint_selection": "validation (three response terms on non-failed cases) + ds_weight x validation cross-entropy",
+    "model": {
+      "sample_rate_hz": 50,
+      "patch_size": 60,
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+      "encoder_conditioning": "none",
+      "decoder_type": "xattn",
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+      "local_snippet": false,
+      "snippet_context": 30,
+      "ground_residual": false,
+      "ground_sign": -1,
+      "amplitude_factorization": true,
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+      "decoder_split": "separate",
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experiment/exp/exp17-building-latent/runs/exp17/cx_RCW/seed20261105/latents.npz

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experiment/exp/exp17-building-latent/runs/exp17/cx_RCW/seed20261105/provenance.json

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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "cx_RCW",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "RCW"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "context",
+      "dim": 32,
+      "prior": 0.0001,
+      "p_zero": 0.2,
+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "context_width": 128,
+      "context_layers": 2,
+      "context_heads": 4,
+      "context_dropout": 0.1,
+      "fit_steps": 0,
+      "fit_steps_select": 0
+    },
+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
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+      20261107
+    ],
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+      6,
+      7,
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+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 18122,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
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+{
+  "git_sha": "b659fe0338df238df5010bb14b44eae9f43c9d96",
+  "variant": "cx_RM",
+  "seed": 20261105,
+  "config": {
+    "version": "exp17-building-latent-v1",
+    "stage": "exp17",
+    "scope": "exp17 (user 2026-11-09): exp16 stage 3 (exp15 weights, frozen ground-motion encoder, decoders fine-tuned per structure type) plus a building vector z for what the declared inputs (type, stories, T1, intensity) do not say: ad_* auto-decoder codes, cx_* context encoder over k records of the same building. Old exp16 variants are kept for provenance only.",
+    "kinds": [
+      "RM"
+    ],
+    "kinds_note": "data.Dataset keeps only these structure types in every split (train, val, testA/B/C, ida).",
+    "resid_window_s": 2,
+    "init_encoder_from": null,
+    "freeze_encoder": true,
+    "init_model_from": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt",
+    "latent": {
+      "mode": "context",
+      "dim": 32,
+      "prior": 0.0001,
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+      "lr_mult": 10,
+      "k_max": 4,
+      "k_select": 2,
+      "context_width": 128,
+      "context_layers": 2,
+      "context_heads": 4,
+      "context_dropout": 0.1,
+      "fit_steps": 0,
+      "fit_steps_select": 0
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+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 4947,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
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experiment/exp/exp17-building-latent/runs/exp17/cx_URM/seed20261105/latents.npz

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experiment/exp/exp17-building-latent/runs/exp17/cx_URM/seed20261105/provenance.json

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+    "init_model_note": "Stage 3 (user 2026-11-08: fine-tune the exp15 decoders per type): init_model_from (path relative to this experiment folder) loads every weight of a checkpoint (strict) and its output scales, so update 0 is exactly that model; validation is also scored at update 0, so the selected checkpoint can be the unchanged source model.",
+    "encoder_note": "init_encoder_from (path relative to this experiment folder) copies the ground-motion encoder (model.temporal) from a checkpoint; freeze_encoder keeps it fixed (no gradient, eval mode, sha256 checked before and after training). Stage 2 (per-type decoders): every type shares the frozen exp15 encoder; condition encoder, both xattn decoders and DS head are trained from scratch on that type only.",
+    "resid_window_note": "residual displacement of a floor = mean relative displacement over the last resid_window_s seconds of the record (same definition as exp15_strata).",
+    "device": "cuda:0",
+    "allowed_gpus": [
+      "H200",
+      "RTX 5090",
+      "RTX PRO 6000"
+    ],
+    "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+    "data_version": "dataset-exp-1 configs/knet_v1.json (mdof_knet_v1)",
+    "seeds": [
+      20261105,
+      20261106,
+      20261107
+    ],
+    "intensity_vocabulary": [
+      0,
+      6,
+      7,
+      8,
+      9
+    ],
+    "structure_type_index": {
+      "RCF": 0,
+      "RCW": 1,
+      "URM": 2,
+      "RM": 3
+    },
+    "structure_features": [
+      {
+        "name": "stories",
+        "transform": "linear",
+        "scale": 10
+      },
+      {
+        "name": "T1_s",
+        "transform": "linear",
+        "scale": 1
+      }
+    ],
+    "model_preset": "base",
+    "latent_note": "exp17 (user 2026-11-09): building vector z (dim 32) added to the condition vector through a zero-initialized linear map, so update 0 = exp15. ad_* = auto-decoder: one learnable code per training building (init N(0, 0.1^2), prior weight x mean squared norm, z = 0 with probability drop so that z = 0 means unknown building); a new building's code is fitted on k of its records with the network frozen (Adam lr fit_lr, fit_steps full-batch steps; fit_steps_select during checkpoint selection). cx_* = context encoder: 7 channels (ground/PGA, relative displacement and acceleration at 1/3, 2/3 and full height, normalized like the decoder outputs), 60-sample patches, 2-layer Transformer width 128, mean over time and over the k records, MLP -> z; training draws k = 0 with probability p_zero, else 1..k_max other records of the same building. New modules use lr x lr_mult and no weight decay. Selection: validation objective on query cases with k_select records per validation building (fixed plan, seed 20261109), update 0 competes. Single choice of every latent setting, not tuned.",
+    "variant_notes": {
+      "rm_base": "Control: exactly the exp15 xattn_roof_ds model and loss, trained on RM only.",
+      "rm_resid": "rm_base + 1.0 x residual displacement term: every floor's residual displacement (mean of the last 2 s), signed, error divided by the case's true peak displacement, Huber delta 0.1.",
+      "rm_ridr": "rm_base + 1.0 x maximum residual inter-story drift ratio term: max over stories of |residual drift|, error divided by the case's true peak inter-story drift ratio, Huber delta 0.1.",
+      "dec_RCF": "Stage 2 (user 2026-11-08: one shared encoder, different decoders): frozen exp15 xattn_roof_ds ground-motion encoder; condition encoder, xattn displacement/acceleration decoders and DS head trained from scratch on RCF only with the rm_resid loss (best of stage 1 on test MSE).",
+      "dec_RCW": "As dec_RCF, RCW only.",
+      "dec_URM": "As dec_RCF, URM only.",
+      "dec_RM": "As dec_RCF, RM only; compared with rm_resid it isolates the effect of the shared frozen encoder.",
+      "ft_RCF": "Stage 3 (user 2026-11-08: fine-tune from the decoders trained on all four types): every weight starts from exp15 xattn_roof_ds, the ground-motion encoder stays frozen; condition encoder, both xattn decoders and DS head are fine-tuned on RCF only with the rm_resid loss (derivative terms at full weight from update 1, as at the end of exp15), lr 1e-4 peak, 200 warmup updates, cosine to 1e-6, about 15 passes clipped to [3000, 20000] updates; update 0 (= exp15) competes in checkpoint selection. Same loss as dec_RCF, so stage 3 vs stage 2 isolates the initialization.",
+      "ft_RCW": "As ft_RCF, RCW only.",
+      "ft_URM": "As ft_RCF, URM only.",
+      "ft_RM": "As ft_RCF, RM only."
+    },
+    "resid_weight_note": "Weight 1.0 (PFA and roof terms use 0.1). Measured on exp15 predictions for 1,024 RM training cases (unweighted): response loss 0.534, residual displacement term 0.031, residual drift term 0.038; at 0.1 each would be under 1% of the loss, at 1.0 about 6-7%. Single choice, not tuned.",
+    "reference": {
+      "exp15_mixed": "../exp15-xattn-roofdisp/runs/exp15/xattn_roof_ds/seed20261105/best.pt"
+    },
+    "reference_note": "exp15 (trained on all four types) evaluated with exp16 codes/run.py --reference on the same RM-only splits: the mixed-training baseline.",
+    "roof_disp_note": "Training only (like PFA): roof_disp_weight x mean over cases of Huber_delta(e)/delta, e = (max_t|u_roof pred| - max_t|u_roof true|)/max(max_t|u_roof true|, roof_disp_floor); roof_disp_floor = training 5th percentile of the roof peak displacement (fitted by run.py).",
+    "ds_head_inputs_note": "'tokens' = exp14 (pooled encoder tokens with condition FiLM + condition); 'tokens+response' adds the mean and max over floors and time of the xattn displacement decoder's last hidden state (LayerNorm) and an MLP of 8 drift features of the predicted displacement (log10 peak drift / DS1-4 limits, log10 peak drift, mean log10 floor peak drift, log10 roof drift ratio, height fraction of the peak floor). Both are detached: the cross-entropy does not change the response decoders. Story heights and DS limits are declared building data.",
+    "velocity_weight": 0.1,
+    "curvature_weight": 0.002,
+    "derivative_ramp": null,
+    "derivative_ramp_note": "null = derivative terms at full weight from update 1; [a, b] = off until update a, linear to full weight at b",
+    "derivative_loss_note": "exp14 F5 (training only): + velocity_weight x per-case normalized MSE of the displacement's first difference x fs + curvature_weight x the same for the second difference x fs^2 (target = the true displacement's second difference = relative acceleration). Selection objective unchanged (three response terms + 0.1 CE).",
+    "train_ds_max": null,
+    "train_ds_max_note": "null = all training cases; an integer keeps only training cases with true DS <= value (validation unchanged)",
+    "loss_floor_fraction": 0.0001,
+    "loss_floor_fraction_note": "per-case loss denominator = max(mean truth^2, (loss_floor_fraction x global scale)^2); exp14 F1 uses 1e-4",
+    "ds_weight": 0.1,
+    "ds_weight_source": "user choice 2026-11-05 (whole-building DS0-4, cross-entropy x 0.1)",
+    "pfa_weight_source": "exp12 frozen 0.1 (as exp13, exp14)",
+    "recipe_source": "exp13 frozen recipe lr 1e-3 / warmup 1000",
+    "steps": 3000,
+    "steps_rule": "about steps_passes (30) passes over the training cases, clipped to steps_clip ([12000, 80000]) updates (exp7/exp13 rule); stage 3 fine-tuning uses 15 passes clipped to [3000, 20000]",
+    "steps_passes": 15,
+    "steps_clip": [
+      3000,
+      20000
+    ],
+    "batch_size": 32,
+    "batch_sampling": "mixed",
+    "learning_rate": 0.0001,
+    "minimum_learning_rate": 1e-06,
+    "warmup_steps": 200,
+    "weight_decay": 1e-05,
+    "max_grad_norm": 1,
+    "normalization": "per_case",
+    "response_weights": [
+      1,
+      1,
+      1
+    ],
+    "scale_fit_cases": 4000,
+    "train_monitor_cases": 2000,
+    "eval_batch": 64,
+    "checkpoint_selection": "validation (three response terms on non-failed cases) + ds_weight x validation cross-entropy",
+    "model": {
+      "sample_rate_hz": 50,
+      "patch_size": 60,
+      "d_model": 256,
+      "heads": 8,
+      "encoder_layers": 2,
+      "decoder_layers": 4,
+      "ff_multiplier": 4,
+      "dropout": 0.05,
+      "numeric_embedding_dim": 64,
+      "category_embedding_dim": 32,
+      "num_structure_types": 4,
+      "encoder_conditioning": "none",
+      "decoder_type": "xattn",
+      "decoder_structure": "film",
+      "floor_fourier_features": 6,
+      "local_snippet": false,
+      "snippet_context": 30,
+      "ground_residual": false,
+      "ground_sign": -1,
+      "amplitude_factorization": true,
+      "concat_query_dim": 32,
+      "concat_hidden_dim": 256,
+      "fno_width": 64,
+      "fno_layers": 4,
+      "fno_mix_modes": 512,
+      "fno_token_channels": 48,
+      "fno_sequence_points": 3600,
+      "decoder_split": "separate",
+      "acceleration_decoder_type": "xattn",
+      "xa_width": 128,
+      "xa_heads": 4,
+      "xa_layers": 4,
+      "xa_query_samples": 10,
+      "xa_snippet_context": 25,
+      "encoder_type": "fno",
+      "encoder_width": null,
+      "encoder_heads": null,
+      "spectral_context": 30,
+      "spectral_bands": 64,
+      "spectral_band_points": 16,
+      "spectral_min_hz": 0.1,
+      "enc_fno_width": 32,
+      "enc_fno_layers": 4,
+      "enc_fno_mix_modes": 1440,
+      "pfa_weight": 0.1,
+      "pfa_huber_delta": 0.1,
+      "roof_disp_weight": 0.1,
+      "roof_disp_huber_delta": 0.1,
+      "resid_disp_weight": 1.0,
+      "resid_disp_huber_delta": 0.1,
+      "resid_drift_weight": 0,
+      "resid_drift_huber_delta": 0.1,
+      "ds_head": true,
+      "ds_hidden_dim": 256,
+      "ds_classes": 5,
+      "ds_head_inputs": "tokens+response",
+      "ds_response_dim": 64
+    },
+    "variant": "cx_URM",
+    "seed": 20261105,
+    "pfa_floor": 0.17890356108546257,
+    "roof_disp_floor": 9.74762599071255e-05,
+    "pidr_floor": 2.5547383639988597e-05,
+    "derivative_scales": {
+      "velocity_m_s": 0.021101134515511526,
+      "curvature_m_s2": 0.3426995778823063
+    },
+    "evaluate_every": 100,
+    "evaluations": 30
+  },
+  "config_sha256": "c47f83396a70c26469db33a18a052b6c4237baaea9998cf8ec003973134046cc",
+  "source_sha256": {
+    "codes/data.py": "74c59224198a74a3406b3e3ebe196588a049b4f8a7d214f8a4da38bd6b96399e",
+    "codes/exp15_report.py": "57e9a4155379359bd9ed5cfbc4c3785e7d8dcfd19af15c4ea03efdce391e2f85",
+    "codes/exp15_strata.py": "44c09cff704187c8db18f55b7aeed7c14c1294bc7bd0a5436a6fdcfbe7b34d76",
+    "codes/exp16_stage3_report.py": "0ce3af57c1141f6842efe15ef96a86bd7c91d7ff86851afe57472415747aadd2",
+    "codes/latent.py": "85918cc54c1e6ddad9a4196abbf0b8b4ae4e79679df15d39332f3c7967fbf5db",
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+    "codes/run.py": "eb48e2f6b9075ddc3810990637b7cbc3d48f6ca186bf19f2ce2d817dadd96924",
+    "codes/src/__init__.py": "0c5782544ecc4792f62d02fc270f8dd8315d427067916d94f80b02bd7205b47e",
+    "codes/src/dataset.py": "e5980a29c96145be6217ca1dd533b67e11a4818abbf2edb2550e3de430ceb8f2",
+    "codes/src/losses.py": "fa328ece5f5c79c58a748fee421ac1f01bf287b1099c09a936ec9f39cac42935",
+    "codes/src/metrics.py": "d77483c685111635f46d4729e135d341a85e38119d1d79eb9f5f049793f396c4"
+  },
+  "data_sha256": {
+    "index.parquet": "05021ad7badb69d6c53b2b2d4174e5270b2a590edcfa51a7afbc6cb7dffd0efb",
+    "buildings.parquet": "11a2c55a280c29194e8b16805e021aee405d199a15f495dca224e8f73436493e",
+    "waveforms.h5": "35e9e2b04859d4dd89eaedd47a1e813376d7fd0b66846201312d93de9591797e"
+  },
+  "data_root": "~/seismic-exp7/experiment/exp/dataset-exp-1/runs/data/mdof_knet_v1",
+  "loss_scales": {
+    "displacement_m": 0.0044371047496964315,
+    "acceleration_m_s2": 0.27100278717237525,
+    "drift_ratio": 0.0007918314563922127
+  },
+  "loss_scale_fit": {
+    "fit_source": "1332 random non-failed training cases (mdof_knet_v1 split train)",
+    "method": "per-building equal-weight RMS; no mean removal",
+    "buildings_seen": 1332,
+    "minimum_scales": [
+      1e-06,
+      0.0001,
+      1e-07
+    ]
+  },
+  "output_scales": [
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+    0.19781670546343402
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+  "initial_state_sha256": "d4c2dbcc504f7ccc55a8f3a3ce0411e1f51a62f74ef1c0de18f45713af7fa9fc",
+  "parameters": 18971769,
+  "torch": "2.14.1+cu130",
+  "gpu": "NVIDIA H200 NVL",
+  "train_cases": 2037,
+  "train_failed_cases": 705,
+  "val_cases": 163,
+  "train_counts_kind_ds": {
+    "URM_DS0": 231,
+    "URM_DS1": 431,
+    "URM_DS3": 119,
+    "URM_DS4": 705,
+    "URM_DS2": 551
+  },
+  "train_buildings": 150,
+  "train_records": 600
+}