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+    "cfgs/default/4m/experiments/t2/3d_timeseries/data_train.yaml": "# AUTO-GENERATED by scripts/gen_matrix_configs.py \u2014 DO NOT EDIT BY HAND.\n# Stage 2 bimodal transfer-matrix cell: 3d x timeseries (S-scale).\n# Rationale + token accounting: decisions/D2-1_bimodal_matrix_design.md\n\nmodalities:\n- tok_3d\n- tok_timeseries\nepoch_size: 50000000\nsampler_type: forced_equal\ntarget_modalities:\n- tok_3d\n- tok_timeseries\nreverse_block_probability: 0.5\nsampling_mode: without_replacement\nsources:\n- name: 3d\n  shards: data/shards/3d_shuffled/train/shard_{000000..000685}.tar\n  weight: 685736\n  modality_tokens:\n    tok_3d: 1875\n  format: npz\n- name: utsd\n  shards: data/shards/utsd_shuffled/train/shard_{000000..002934}.tar\n  weight: 2934667\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: forecasting\n  shards: data/shards/forecasting_shuffled/train/shard_{000000..000098}.tar\n  weight: 98423\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: timeseries\n  shards: data/shards/timeseries_shuffled/train/shard_{000000..000109}.tar\n  weight: 109190\n  modality_tokens:\n    tok_timeseries: 14\n  format: npz\n- name: timeseries_pile\n  shards: data/shards/timeseries_pile_shuffled/train/shard_{000000..005159}.tar\n  weight: 5159748\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: time300b\n  shards: data/shards/time300b_shuffled/train/shard_{000000..017340}.tar\n  weight: 17340008\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n",
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+    "distributed": true,
+    "dist_backend": "nccl",
+    "num_tasks": 1,
+    "sampling_mode": "without_replacement",
+    "max_micro_steps": 314942,
+    "lossbearing_target": 10320000000.0,
+    "lossbearing_mode": true,
+    "lr": 0.0014142135623730952,
+    "min_lr": 7.071e-05,
+    "global_micro_step": 314942,
+    "cum_lossbearing": 10322953397.0,
+    "cum_mod_tokens": {
+      "text": 163435381,
+      "tok_timeseries": 5118167604,
+      "tok_3d": 5041350412
+    }
+  },
+  "source_configs": {
+    "cfgs/default/4m/experiments/confound/pairs_lr/3d_timeseries/run_eb2048_lr1.41e-3.yaml": "# STEP-MATCHED PAIR, LR ARM t2_3d_timeseries_s_eb2048_lr1.41e-3 (confound program stage 1 \u2014 D6-2 \u00a76b.1).\n# The step-matched pair (effective batch 2048) at absolute LR 0.001414 = sqrt(2) x 0.001.\n# With unrelated gradients Adam shrinks each modality's step on the SHARED weights by ~1/sqrt(2);\n# this LR restores it. Partner cost recovered here = shared-update budget, not capacity/content.\n# Private embeddings/heads also get the higher LR: read against the recipients' solo LR response.\n_base_: ../../../../models/dec/4m-s-dec.yaml\nrun_name: t2_3d_timeseries_s_eb2048_lr1.41e-3\ntotal_tokens: -1\nlossbearing_tokens: 10.32\ndata_config: cfgs/default/4m/experiments/t2/3d_timeseries/data_train.yaml\ndata_config_val: cfgs/default/4m/experiments/t2/3d_timeseries/data_val.yaml\nwarmup_steps: 250\neval_freq_tokens: 2\nsave_ckpt_freq_tokens: 5\nwandb_group: cc-pairs\neffective_batch_size: 2048\nlr_batch_scaling: none\nblr: 0.0014142135623730952\nseed: 0\naccum_iter: 64\nmin_blr: 7.071e-05\n",
+    "cfgs/default/4m/experiments/t2/3d_timeseries/data_train.yaml": "# AUTO-GENERATED by scripts/gen_matrix_configs.py \u2014 DO NOT EDIT BY HAND.\n# Stage 2 bimodal transfer-matrix cell: 3d x timeseries (S-scale).\n# Rationale + token accounting: decisions/D2-1_bimodal_matrix_design.md\n\nmodalities:\n- tok_3d\n- tok_timeseries\nepoch_size: 50000000\nsampler_type: forced_equal\ntarget_modalities:\n- tok_3d\n- tok_timeseries\nreverse_block_probability: 0.5\nsampling_mode: without_replacement\nsources:\n- name: 3d\n  shards: data/shards/3d_shuffled/train/shard_{000000..000685}.tar\n  weight: 685736\n  modality_tokens:\n    tok_3d: 1875\n  format: npz\n- name: utsd\n  shards: data/shards/utsd_shuffled/train/shard_{000000..002934}.tar\n  weight: 2934667\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: forecasting\n  shards: data/shards/forecasting_shuffled/train/shard_{000000..000098}.tar\n  weight: 98423\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: timeseries\n  shards: data/shards/timeseries_shuffled/train/shard_{000000..000109}.tar\n  weight: 109190\n  modality_tokens:\n    tok_timeseries: 14\n  format: npz\n- name: timeseries_pile\n  shards: data/shards/timeseries_pile_shuffled/train/shard_{000000..005159}.tar\n  weight: 5159748\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: time300b\n  shards: data/shards/time300b_shuffled/train/shard_{000000..017340}.tar\n  weight: 17340008\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n",
+    "cfgs/default/4m/experiments/t2/3d_timeseries/data_val.yaml": "# AUTO-GENERATED by scripts/gen_matrix_configs.py \u2014 DO NOT EDIT BY HAND.\n# Stage 2 bimodal transfer-matrix cell: 3d x timeseries (S-scale).\n# Rationale + token accounting: decisions/D2-1_bimodal_matrix_design.md\n\nmodalities:\n- tok_3d\n- tok_timeseries\nval_steps: 200\nsampler_type: uniform\nsources:\n- name: 3d_val\n  shards: data/shards/3d_shuffled/val/shard_{000000..000013}.tar\n  weight: 14000\n  modality_tokens:\n    tok_3d: 1875\n  format: npz\n- name: utsd_val\n  shards: data/shards/utsd_shuffled/val/shard_{000000..000049}.tar\n  weight: 50000\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: forecasting_val\n  shards: data/shards/forecasting_shuffled/val/shard_{000000..000001}.tar\n  weight: 2000\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: timeseries_val\n  shards: data/shards/timeseries_shuffled/val/shard_{000000..000002}.tar\n  weight: 2200\n  modality_tokens:\n    tok_timeseries: 14\n  format: npz\n- name: timeseries_pile_val\n  shards: data/shards/timeseries_pile_shuffled/val/shard_{000000..000049}.tar\n  weight: 50000\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n- name: time300b_val\n  shards: data/shards/time300b_shuffled/val/shard_{000000..000049}.tar\n  weight: 50000\n  modality_tokens:\n    tok_timeseries: 64\n  format: npz\n"
+  }
+}