ablation: add Group A (aggregator) + Group B (architecture) infrastructure

Extends MixedCFMConfig with 5 backwards-compatible flags (use_flow_token,
n_packet_tokens, disc_as_cont, cont_as_disc + cont_n_bins) so existing
JANUS-full checkpoints load with 0 missing/unexpected keys.

Adds:
- 60 ablation training configs (5 variants × 4 datasets × 3 seeds)
- scripts/ablation/{generate_configs.py, run_groupB.sh, run_cross_groupB.sh,
  smoke_test.sh} — config generation + GPU drivers
- scripts/aggregate/aggregate_ablation{,_cross,_cross_B}.py — produces
  within-dataset and cross-dataset (3×3) ablation tables with 3-seed mean
  ± 95% t-CI plus optional paired DeLong p-values

README updated with ablation section pointing at
artifacts/ablation/ABLATION_SUMMARY.md.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Mixed_CFM/configs/ablation/b1_noflow/cicddos2019_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed42_b1_noflow
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/cicddos2019_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed43_b1_noflow
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/cicddos2019_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed44_b1_noflow
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/cicids2017_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed42_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/cicids2017_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed43_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/cicids2017_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed44_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/ciciot2023_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed42_b1_noflow
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+device: auto
+reference_mode: causal_packets
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/ciciot2023_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed43_b1_noflow
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+device: auto
+reference_mode: causal_packets
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/ciciot2023_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed44_b1_noflow
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+device: auto
+reference_mode: causal_packets
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/iscxtor2016_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed42_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/iscxtor2016_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed43_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b1_noflow/iscxtor2016_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed44_b1_noflow
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+use_flow_token: false

Mixed_CFM/configs/ablation/b2_flowonly/cicddos2019_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed42_b2_flowonly
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/cicddos2019_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed43_b2_flowonly
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/cicddos2019_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed44_b2_flowonly
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/cicids2017_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed42_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/cicids2017_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed43_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/cicids2017_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed44_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+d_model: 128
+n_layers: 4
+n_heads: 4
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+eval_n: 20000
+eval_batch_size: 512
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/ciciot2023_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed42_b2_flowonly
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
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+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+device: auto
+reference_mode: causal_packets
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/ciciot2023_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed43_b2_flowonly
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
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+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+device: auto
+reference_mode: causal_packets
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/ciciot2023_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed44_b2_flowonly
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
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+n_train: 10000
+min_len: 2
+seed: 44
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
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+reference_mode: causal_packets
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/iscxtor2016_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed42_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/iscxtor2016_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed43_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+n_packet_tokens: 0

Mixed_CFM/configs/ablation/b2_flowonly/iscxtor2016_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed44_b2_flowonly
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
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Mixed_CFM/configs/ablation/b3_allcont/cicddos2019_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed42_b3_allcont
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/cicddos2019_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed43_b3_allcont
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
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+lambda_disc: 0.0
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+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/cicddos2019_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed44_b3_allcont
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b3_allcont/cicids2017_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed42_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
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+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/cicids2017_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed43_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b3_allcont/cicids2017_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed44_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
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+reference_mode: causal_packets
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Mixed_CFM/configs/ablation/b3_allcont/ciciot2023_seed42.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed42_b3_allcont
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
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+reference_mode: causal_packets
+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/ciciot2023_seed43.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed43_b3_allcont
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
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+reference_mode: causal_packets
+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/ciciot2023_seed44.yaml

@@ -0,0 +1,36 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed44_b3_allcont
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
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+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/iscxtor2016_seed42.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed42_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
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+lambda_disc: 0.0
+reference_mode: causal_packets
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+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/iscxtor2016_seed43.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed43_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
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+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+disc_as_cont: true

Mixed_CFM/configs/ablation/b3_allcont/iscxtor2016_seed44.yaml

@@ -0,0 +1,34 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed44_b3_allcont
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
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+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto
+disc_as_cont: true

Mixed_CFM/configs/ablation/b4_alldisc/cicddos2019_seed42.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed42_b4_alldisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
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+eval_n: 10000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+cont_as_disc: true
+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/cicddos2019_seed43.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed43_b4_alldisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 20000
+attack_cap: 20000
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+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
+device: auto
+cont_as_disc: true
+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/cicddos2019_seed44.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed44_b4_alldisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
+flow_features_align: auto
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+n_train: 10000
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+val_cap: 20000
+attack_cap: 20000
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+lambda_disc: 1.0
+reference_mode: causal_packets
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+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/cicids2017_seed42.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed42_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
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+use_ot: true
+lambda_disc: 1.0
+reference_mode: causal_packets
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+cont_as_disc: true
+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/cicids2017_seed43.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed43_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
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+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/cicids2017_seed44.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed44_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
+flow_features_align: auto
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+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/ciciot2023_seed42.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed42_b4_alldisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
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+cont_as_disc: true
+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/ciciot2023_seed43.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed43_b4_alldisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b4_alldisc/ciciot2023_seed44.yaml

@@ -0,0 +1,37 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed44_b4_alldisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b4_alldisc/iscxtor2016_seed42.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed42_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
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+n_disc_classes: 8

Mixed_CFM/configs/ablation/b4_alldisc/iscxtor2016_seed43.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed43_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
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Mixed_CFM/configs/ablation/b4_alldisc/iscxtor2016_seed44.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed44_b4_alldisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicddos2019_seed42.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed42_b5_nodisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicddos2019_seed43.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed43_b5_nodisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicddos2019/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicddos2019_seed44.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicddos2019_seed44_b5_nodisc
+source_store: /home/chy/JANUS/datasets/cicddos2019/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/cicddos2019/processed/flows.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicids2017_seed42.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed42_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicids2017_seed43.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed43_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
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Mixed_CFM/configs/ablation/b5_nodisc/cicids2017_seed44.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_cicids2017_seed44_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/cicids2017/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/cicids2017/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/cicids2017/processed/flow_features.parquet
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+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto

Mixed_CFM/configs/ablation/b5_nodisc/ciciot2023_seed42.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed42_b5_nodisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+device: auto
+reference_mode: causal_packets

Mixed_CFM/configs/ablation/b5_nodisc/ciciot2023_seed43.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed43_b5_nodisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+device: auto
+reference_mode: causal_packets

Mixed_CFM/configs/ablation/b5_nodisc/ciciot2023_seed44.yaml

@@ -0,0 +1,35 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_ciciot2023_seed44_b5_nodisc
+source_store: /home/chy/JANUS/datasets/ciciot2023/processed/full_store
+flows_parquet: /home/chy/JANUS/datasets/ciciot2023/processed/full_store/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/ciciot2023/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: normal
+val_cap: 10000
+attack_cap: 20000
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+device: auto
+reference_mode: causal_packets

Mixed_CFM/configs/ablation/b5_nodisc/iscxtor2016_seed42.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed42_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 42
+data_seed: 42
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto

Mixed_CFM/configs/ablation/b5_nodisc/iscxtor2016_seed43.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed43_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 43
+data_seed: 43
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto

Mixed_CFM/configs/ablation/b5_nodisc/iscxtor2016_seed44.yaml

@@ -0,0 +1,33 @@
+save_dir: /home/chy/JANUS/artifacts/ablation/janus_iscxtor2016_seed44_b5_nodisc
+packets_npz: /home/chy/JANUS/datasets/iscxtor2016/processed/packets.npz
+flows_parquet: /home/chy/JANUS/datasets/iscxtor2016/processed/flows.parquet
+flow_features_path: /home/chy/JANUS/datasets/iscxtor2016/processed/flow_features.parquet
+flow_features_align: auto
+T: 64
+n_train: 10000
+min_len: 2
+seed: 44
+data_seed: 44
+train_ratio: 0.8
+benign_label: nontor
+d_model: 128
+n_layers: 4
+n_heads: 4
+mlp_ratio: 4.0
+time_dim: 64
+token_dim: null
+batch_size: 256
+num_workers: 0
+epochs: 50
+lr: 0.0003
+weight_decay: 0.01
+grad_clip: 1.0
+eval_every: 10
+eval_n: 20000
+eval_batch_size: 512
+eval_n_steps: 8
+sigma: 0.1
+use_ot: true
+lambda_disc: 0.0
+reference_mode: causal_packets
+device: auto

Mixed_CFM/eval_cross.py

@@ -20,7 +20,7 @@ def _device(arg: str) -> torch.device:
         return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     return torch.device(arg)
 
-def _score_batch(model, flow_z, cont_z, disc_int, lens, device, batch_size=256, n_steps=16):
+def _score_batch(model, flow_z, cont_z, disc_int, lens, device, batch_size=256, n_steps=16, cont_bin_edges=None):
     out: dict[str, list[np.ndarray]] = {}
     for start in range(0, len(flow_z), batch_size):
         sl = slice(start, start + batch_size)
@@ -29,8 +29,8 @@ def _score_batch(model, flow_z, cont_z, disc_int, lens, device, batch_size=256,
         d = torch.from_numpy(disc_int[sl]).long().to(device)
         l = torch.from_numpy(lens[sl]).long().to(device)
         with torch.no_grad():
-            traj = model.trajectory_metrics(f, c, d, l, n_steps=n_steps)
-            nll = model.disc_nll_score(f, c, d, l)
+            traj = model.trajectory_metrics(f, c, d, l, n_steps=n_steps, cont_bin_edges=cont_bin_edges)
+            nll = model.disc_nll_score(f, c, d, l, cont_bin_edges=cont_bin_edges)
         for src in (traj, nll):
             for (k, v) in src.items():
                 out.setdefault(k, []).append(v.detach().cpu().numpy())
@@ -63,6 +63,10 @@ def main() -> None:
     model = MixedTokenCFM(model_cfg).to(device)
     model.load_state_dict(ckpt['model_state_dict'])
     model.eval()
+    cont_bin_edges = None
+    if 'cont_bin_edges' in ckpt:
+        cont_bin_edges = torch.from_numpy(np.asarray(ckpt['cont_bin_edges'])).to(device)
+        print(f'[model] cont_bin_edges shape={tuple(cont_bin_edges.shape)} (B4 mode; src edges applied to target)')
     cont_mean = np.asarray(ckpt['cont_mean'], dtype=np.float32)
     cont_std = np.asarray(ckpt['cont_std'], dtype=np.float32)
     flow_mean = np.asarray(ckpt['flow_mean'], dtype=np.float32)
@@ -140,11 +144,11 @@ def main() -> None:
     a_flow_z = ((a_flow - flow_mean) / np.maximum(flow_std, 1e-06)).astype(np.float32)
     t0 = time.time()
     print('[eval] benign...')
-    b_scores = _score_batch(model, b_flow_z, b_cont, b_disc, b_len, device, batch_size=args.batch_size, n_steps=args.n_steps)
+    b_scores = _score_batch(model, b_flow_z, b_cont, b_disc, b_len, device, batch_size=args.batch_size, n_steps=args.n_steps, cont_bin_edges=cont_bin_edges)
     print(f'[eval] benign done {time.time() - t0:.1f}s')
     t0 = time.time()
     print('[eval] attack...')
-    a_scores = _score_batch(model, a_flow_z, a_cont, a_disc, a_len, device, batch_size=args.batch_size, n_steps=args.n_steps)
+    a_scores = _score_batch(model, a_flow_z, a_cont, a_disc, a_len, device, batch_size=args.batch_size, n_steps=args.n_steps, cont_bin_edges=cont_bin_edges)
     print(f'[eval] attack done {time.time() - t0:.1f}s')
     keys = sorted(set(b_scores) & set(a_scores))
     overall = {}

Mixed_CFM/eval_phase1.py

@@ -18,7 +18,7 @@ def _device(arg: str) -> torch.device:
         return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     return torch.device(arg)
 
-def _score_batch(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int) -> dict[str, np.ndarray]:
+def _score_batch(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int, cont_bin_edges: torch.Tensor | None = None) -> dict[str, np.ndarray]:
     out: dict[str, list[np.ndarray]] = {}
     for start in range(0, len(flow_np), batch_size):
         sl = slice(start, start + batch_size)
@@ -27,8 +27,8 @@ def _score_batch(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray,
         disc = torch.from_numpy(disc_np[sl]).long().to(device)
         lens = torch.from_numpy(len_np[sl]).long().to(device)
         with torch.no_grad():
-            traj = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps)
-            nll = model.disc_nll_score(flow, cont, disc, lens)
+            traj = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps, cont_bin_edges=cont_bin_edges)
+            nll = model.disc_nll_score(flow, cont, disc, lens, cont_bin_edges=cont_bin_edges)
         for d in (traj, nll):
             for (k, v) in d.items():
                 out.setdefault(k, []).append(v.detach().cpu().numpy())
@@ -65,7 +65,11 @@ def main() -> None:
     model = MixedTokenCFM(model_cfg).to(device)
     model.load_state_dict(ckpt['model_state_dict'])
     model.eval()
-    print(f'[model] T={model_cfg.T} flow_dim={model_cfg.flow_dim}')
+    cont_bin_edges = None
+    if 'cont_bin_edges' in ckpt:
+        cont_bin_edges = torch.from_numpy(np.asarray(ckpt['cont_bin_edges'])).to(device)
+        print(f'[model] cont_bin_edges shape={tuple(cont_bin_edges.shape)} (B4 mode)')
+    print(f'[model] T={model_cfg.T} flow_dim={model_cfg.flow_dim} use_flow_token={model_cfg.use_flow_token} n_packet_tokens={model_cfg.n_packet_tokens} disc_as_cont={model_cfg.disc_as_cont} cont_as_disc={model_cfg.cont_as_disc}')
     data = load_mixed_data(packets_npz=Path(cfg['packets_npz']) if cfg.get('packets_npz') else None, source_store=Path(cfg['source_store']) if cfg.get('source_store') else None, flows_parquet=Path(cfg['flows_parquet']), flow_features_path=Path(cfg['flow_features_path']), flow_features_align=str(cfg.get('flow_features_align', 'auto')), T=int(cfg['T']), split_seed=int(cfg.get('data_seed', cfg.get('seed', 42))), train_ratio=float(cfg.get('train_ratio', 0.8)), benign_label=str(cfg.get('benign_label', 'normal')), min_len=int(cfg.get('min_len', 2)), attack_cap=int(cfg['attack_cap']) if cfg.get('attack_cap') else None, val_cap=int(cfg['val_cap']) if cfg.get('val_cap') else None)
     print(f'[data] val={len(data.val_flow):,} attack={len(data.attack_flow):,}')
     rng = np.random.default_rng(0)
@@ -81,10 +85,10 @@ def main() -> None:
         atk_labels = atk_labels[idx]
     print(f'[eval] scoring val={len(val_flow):,} atk={len(atk_flow):,}')
     t0 = time.time()
-    val = _score_batch(model, val_flow, val_cont, val_disc, val_len, device, batch_size=args.batch_size, n_steps=args.n_steps)
+    val = _score_batch(model, val_flow, val_cont, val_disc, val_len, device, batch_size=args.batch_size, n_steps=args.n_steps, cont_bin_edges=cont_bin_edges)
     print(f'[eval] val done {time.time() - t0:.1f}s')
     t0 = time.time()
-    atk = _score_batch(model, atk_flow, atk_cont, atk_disc, atk_len, device, batch_size=args.batch_size, n_steps=args.n_steps)
+    atk = _score_batch(model, atk_flow, atk_cont, atk_disc, atk_len, device, batch_size=args.batch_size, n_steps=args.n_steps, cont_bin_edges=cont_bin_edges)
     print(f'[eval] atk done {time.time() - t0:.1f}s')
     keys = sorted(set(val) & set(atk))
     overall: dict[str, dict[str, float]] = {}

Mixed_CFM/model.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import math
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -19,6 +19,7 @@ AdaLNBlock = _unified.AdaLNBlock
 SinusoidalTimeEmb = _unified.SinusoidalTimeEmb
 _sinkhorn_coupling = _unified._sinkhorn_coupling
 
+
 @dataclass
 class MixedCFMConfig:
     T: int = 64
@@ -40,6 +41,11 @@ class MixedCFMConfig:
     lambda_disc: float = 1.0
     disc_path: str = 'uniform'
     disc_embed_scale: float = 1.0
+    # ---- B-group ablation flags (defaults preserve JANUS-full behavior) ----
+    use_flow_token: bool = True       # B1: False removes the [FLOW] token
+    n_packet_tokens: int = -1         # B2: 0 removes packet tokens entirely; -1 = use cfg.T
+    disc_as_cont: bool = False        # B3: feed 6 disc bits through CFM head as continuous values
+    cont_as_disc: bool = False        # B4: quantize 3 cont channels into n_disc_classes bins (mask-pred only)
 
     def __post_init__(self) -> None:
         if len(self.cont_pkt_idx) != self.n_cont_pkt:
@@ -48,10 +54,13 @@ class MixedCFMConfig:
             raise ValueError('disc_pkt_idx length mismatch n_disc_pkt')
         if self.disc_path != 'uniform':
             raise NotImplementedError(f'disc_path={self.disc_path}')
+        if self.disc_as_cont and self.cont_as_disc:
+            raise ValueError('disc_as_cont and cont_as_disc are mutually exclusive')
+
 
 class MixedVelocity(nn.Module):
 
-    def __init__(self, token_dim: int, seq_len: int, n_disc: int, n_classes: int, d_model: int=128, n_layers: int=4, n_heads: int=4, mlp_ratio: float=4.0, time_dim: int=64, reference_mode: str | None=None) -> None:
+    def __init__(self, token_dim: int, seq_len: int, n_disc: int, n_classes: int, d_model: int=128, n_layers: int=4, n_heads: int=4, mlp_ratio: float=4.0, time_dim: int=64, reference_mode: str | None=None, has_flow_token: bool=True) -> None:
         super().__init__()
         if reference_mode not in (None, 'causal_packets', 'causal_all'):
             raise ValueError(f'reference_mode={reference_mode!r}')
@@ -60,6 +69,7 @@ class MixedVelocity(nn.Module):
         self.n_disc = n_disc
         self.n_classes = n_classes
         self.reference_mode = reference_mode
+        self.has_flow_token = has_flow_token
         self.input_proj = nn.Linear(token_dim, d_model)
         self.pos_emb = nn.Parameter(torch.zeros(1, seq_len, d_model))
         self.type_emb = nn.Embedding(2, d_model)
@@ -70,12 +80,15 @@ class MixedVelocity(nn.Module):
         self.blocks = nn.ModuleList([AdaLNBlock(d_model, n_heads, mlp_ratio, cond_dim=d_model) for _ in range(n_layers)])
         self.out_norm = nn.LayerNorm(d_model, elementwise_affine=False)
         self.head_v = nn.Linear(d_model, token_dim)
-        self.head_disc = nn.Linear(d_model, n_disc * n_classes)
+        # head_disc only meaningful when n_disc > 0
+        out_disc = max(n_disc * n_classes, 1)
+        self.head_disc = nn.Linear(d_model, out_disc)
         for layer in (self.head_v, self.head_disc):
             nn.init.zeros_(layer.weight)
             nn.init.zeros_(layer.bias)
         type_ids = torch.ones(seq_len, dtype=torch.long)
-        type_ids[0] = 0
+        if has_flow_token and seq_len >= 1:
+            type_ids[0] = 0
         self.register_buffer('type_ids', type_ids, persistent=False)
 
     def _attn_mask(self, L: int, device: torch.device) -> torch.Tensor | None:
@@ -83,8 +96,11 @@ class MixedVelocity(nn.Module):
             return None
         if self.reference_mode == 'causal_packets':
             mask = torch.zeros((L, L), dtype=torch.bool, device=device)
-            if L > 1:
-                mask[1:, 1:] = torch.triu(torch.ones(L - 1, L - 1, dtype=torch.bool, device=device), diagonal=1)
+            offset = 1 if self.has_flow_token else 0
+            if L > offset:
+                M = L - offset
+                if M > 1:
+                    mask[offset:, offset:] = torch.triu(torch.ones(M, M, dtype=torch.bool, device=device), diagonal=1)
             return mask
         return torch.triu(torch.ones(L, L, dtype=torch.bool, device=device), diagonal=1)
 
@@ -100,143 +116,339 @@ class MixedVelocity(nn.Module):
             h = block(h, cond, key_padding_mask, attn_mask=attn_mask)
         h = self.out_norm(h)
         v = self.head_v(h)
-        d = self.head_disc(h).view(B, L, self.n_disc, self.n_classes)
+        if self.n_disc > 0:
+            d = self.head_disc(h).view(B, L, self.n_disc, self.n_classes)
+        else:
+            d = h.new_zeros((B, L, 0, self.n_classes))
         return (v, d)
 
+
 class MixedTokenCFM(nn.Module):
 
     def __init__(self, cfg: MixedCFMConfig) -> None:
         super().__init__()
         self.cfg = cfg
-        cont_size = cfg.n_cont_pkt + cfg.n_disc_pkt
+        # Effective packet count (B2: n_packet_tokens=0 → no packets)
+        self.eff_T = cfg.T if cfg.n_packet_tokens < 0 else int(cfg.n_packet_tokens)
+        if not cfg.use_flow_token and self.eff_T == 0:
+            raise ValueError('cannot disable both FLOW token and packet tokens')
+        # Effective per-packet feature split
+        if cfg.disc_as_cont:
+            # B3: 9 cont, 0 disc (CFM head only)
+            self.eff_n_cont = cfg.n_cont_pkt + cfg.n_disc_pkt
+            self.eff_n_disc = 0
+        elif cfg.cont_as_disc:
+            # B4: 0 cont, 9 disc (mask-pred head only)
+            self.eff_n_cont = 0
+            self.eff_n_disc = cfg.n_cont_pkt + cfg.n_disc_pkt
+        else:
+            self.eff_n_cont = cfg.n_cont_pkt
+            self.eff_n_disc = cfg.n_disc_pkt
+        cont_size = self.eff_n_cont + self.eff_n_disc
+        # Token layout: [type_flag(1) | flow_dim or cont_size]
         self.token_dim = cfg.token_dim or 1 + max(cfg.flow_dim, cont_size)
         if self.token_dim < 1 + max(cfg.flow_dim, cont_size):
             raise ValueError('token_dim too small')
-        self.seq_len = cfg.T + 1
-        self.velocity = MixedVelocity(token_dim=self.token_dim, seq_len=self.seq_len, n_disc=cfg.n_disc_pkt, n_classes=cfg.n_disc_classes, d_model=cfg.d_model, n_layers=cfg.n_layers, n_heads=cfg.n_heads, mlp_ratio=cfg.mlp_ratio, time_dim=cfg.time_dim, reference_mode=cfg.reference_mode)
+        self.seq_len = (1 if cfg.use_flow_token else 0) + self.eff_T
+        self.velocity = MixedVelocity(
+            token_dim=self.token_dim, seq_len=self.seq_len,
+            n_disc=self.eff_n_disc, n_classes=cfg.n_disc_classes,
+            d_model=cfg.d_model, n_layers=cfg.n_layers, n_heads=cfg.n_heads,
+            mlp_ratio=cfg.mlp_ratio, time_dim=cfg.time_dim,
+            reference_mode=cfg.reference_mode, has_flow_token=cfg.use_flow_token,
+        )
 
+    # ------------------------------------------------------------------ #
+    # token assembly                                                     #
+    # ------------------------------------------------------------------ #
     def _embed_disc(self, x_disc_int: torch.Tensor) -> torch.Tensor:
+        n = self.cfg.n_disc_classes
         s = self.cfg.disc_embed_scale
-        return (x_disc_int.float() - 0.5) * s
+        if n <= 1:
+            return x_disc_int.float() * 0.0
+        # Map integers in [0, n-1] to centered floats in [-s/2, +s/2].
+        # Backwards-compatible with old (x - 0.5)*s formula when n=2.
+        return (x_disc_int.float() / (n - 1) - 0.5) * s
+
+    def _flow_dim(self) -> int:
+        return self.cfg.flow_dim
 
     def build_tokens(self, flow: torch.Tensor, packets_cont: torch.Tensor, x_disc_t_int: torch.Tensor) -> torch.Tensor:
-        (B, T, Cp) = packets_cont.shape
-        assert T == self.cfg.T and Cp == self.cfg.n_cont_pkt
-        z = packets_cont.new_zeros((B, T + 1, self.token_dim))
-        z[:, 0, 0] = -1.0
-        z[:, 0, 1:1 + self.cfg.flow_dim] = flow
-        z[:, 1:, 0] = 1.0
-        z[:, 1:, 1:1 + self.cfg.n_cont_pkt] = packets_cont
-        z[:, 1:, 1 + self.cfg.n_cont_pkt:1 + self.cfg.n_cont_pkt + self.cfg.n_disc_pkt] = self._embed_disc(x_disc_t_int)
+        """Assemble [B, seq_len, token_dim].
+
+        packets_cont: [B, eff_T, eff_n_cont] (may be empty in last dim)
+        x_disc_t_int: [B, eff_T, eff_n_disc] integer ids in [0, n_disc_classes-1]
+        """
+        B = flow.shape[0]
+        device = flow.device
+        T = self.eff_T
+        z = flow.new_zeros((B, self.seq_len, self.token_dim))
+        cur = 0
+        if self.cfg.use_flow_token:
+            z[:, 0, 0] = -1.0  # type flag
+            z[:, 0, 1:1 + self._flow_dim()] = flow
+            cur = 1
+        if T > 0:
+            z[:, cur:cur + T, 0] = 1.0  # type flag
+            base = 1
+            if self.eff_n_cont > 0:
+                z[:, cur:cur + T, base:base + self.eff_n_cont] = packets_cont
+                base += self.eff_n_cont
+            if self.eff_n_disc > 0:
+                z[:, cur:cur + T, base:base + self.eff_n_disc] = self._embed_disc(x_disc_t_int)
         return z
 
     def key_padding_mask(self, lens: torch.Tensor) -> torch.Tensor:
         B = lens.shape[0]
-        idx = torch.arange(self.cfg.T, device=lens.device)[None, :]
-        packet_real = idx < lens[:, None]
-        real = torch.cat([torch.ones(B, 1, dtype=torch.bool, device=lens.device), packet_real], dim=1)
+        device = lens.device
+        T = self.eff_T
+        pieces = []
+        if self.cfg.use_flow_token:
+            pieces.append(torch.ones(B, 1, dtype=torch.bool, device=device))
+        if T > 0:
+            idx = torch.arange(T, device=device)[None, :]
+            pieces.append(idx < lens[:, None])
+        real = torch.cat(pieces, dim=1) if pieces else torch.ones(B, 0, dtype=torch.bool, device=device)
         return ~real
 
     def _loss_mask(self, lens: torch.Tensor) -> torch.Tensor:
         return (~self.key_padding_mask(lens)).float()
 
-    def compute_loss(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, *, return_components: bool=False) -> torch.Tensor | dict[str, torch.Tensor]:
-        (B, T, _) = packets_cont.shape
-        device = packets_cont.device
+    # ------------------------------------------------------------------ #
+    # B4 helper: quantize cont -> integer bins                           #
+    # ------------------------------------------------------------------ #
+    def quantize_cont(self, packets_cont: torch.Tensor, bin_edges: torch.Tensor) -> torch.Tensor:
+        """packets_cont [B, T, n_cont_orig] (already z-scored); bin_edges [n_cont_orig, n_classes-1]
+        returns int64 [B, T, n_cont_orig] in [0, n_classes-1]."""
+        B, T, C = packets_cont.shape
+        out = torch.zeros((B, T, C), dtype=torch.long, device=packets_cont.device)
+        for c in range(C):
+            edges = bin_edges[c]  # [n_classes-1]
+            # bucketize: returns 0..n for n edges
+            out[:, :, c] = torch.bucketize(packets_cont[:, :, c].contiguous(), edges)
+        out.clamp_(0, self.cfg.n_disc_classes - 1)
+        return out
+
+    # ------------------------------------------------------------------ #
+    # Loss                                                               #
+    # ------------------------------------------------------------------ #
+    def compute_loss(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, *, return_components: bool=False, cont_bin_edges: torch.Tensor | None=None) -> torch.Tensor | dict[str, torch.Tensor]:
+        cfg = self.cfg
+        B = flow.shape[0]
+        T = self.eff_T
+        device = flow.device
+
+        # Resolve effective cont/disc tensors per ablation mode
+        if cfg.disc_as_cont:
+            # 9 cont = original 3 cont + 6 disc-as-float
+            disc_as_cont_float = self._embed_disc(packets_disc) if T > 0 else None
+            if T > 0:
+                eff_cont = torch.cat([packets_cont, disc_as_cont_float], dim=-1) if cfg.n_cont_pkt > 0 else disc_as_cont_float
+            else:
+                eff_cont = packets_cont.new_zeros((B, 0, 0))
+            eff_disc_int = torch.zeros((B, T, 0), dtype=torch.long, device=device)
+        elif cfg.cont_as_disc:
+            # 0 cont, 9 disc: quantize cont via supplied bin_edges
+            if T > 0:
+                if cont_bin_edges is None:
+                    raise ValueError('cont_as_disc requires cont_bin_edges')
+                cont_int = self.quantize_cont(packets_cont, cont_bin_edges)
+                eff_disc_int = torch.cat([cont_int, packets_disc.long()], dim=-1)
+            else:
+                eff_disc_int = torch.zeros((B, 0, self.eff_n_disc), dtype=torch.long, device=device)
+            eff_cont = flow.new_zeros((B, T, 0))
+        else:
+            eff_cont = packets_cont if T > 0 else packets_cont.new_zeros((B, 0, cfg.n_cont_pkt))
+            eff_disc_int = packets_disc.long() if T > 0 else torch.zeros((B, 0, cfg.n_disc_pkt), dtype=torch.long, device=device)
+
+        # Build x_1 (data tokens; mask-pred path uses zero ids for disc at packet positions during CFM regression)
+        zero_disc = torch.zeros_like(eff_disc_int)
+        x_1_cont = self.build_tokens(flow, eff_cont, zero_disc)
+
         mask = self._loss_mask(lens)
         kpm = mask == 0
-        x_1_cont = self.build_tokens(flow, packets_cont, torch.zeros_like(packets_disc))
+
         x_0_cont = torch.randn_like(x_1_cont)
-        if self.cfg.use_ot:
+
+        if cfg.use_ot:
             flat0 = (x_0_cont * mask[:, :, None]).reshape(B, -1)
             flat1 = (x_1_cont * mask[:, :, None]).reshape(B, -1)
             col = _sinkhorn_coupling(torch.cdist(flat0.float(), flat1.float()))
             x_1_cont = x_1_cont[col]
-            packets_cont = packets_cont[col]
+            eff_cont = eff_cont[col] if eff_cont.numel() > 0 else eff_cont
+            eff_disc_int = eff_disc_int[col] if eff_disc_int.numel() > 0 else eff_disc_int
             packets_disc = packets_disc[col]
             flow = flow[col]
             lens = lens[col]
             mask = self._loss_mask(lens)
             kpm = mask == 0
+
         t = torch.rand(B, device=device)
         x_t_cont = (1.0 - t[:, None, None]) * x_0_cont + t[:, None, None] * x_1_cont
-        if self.cfg.sigma > 0:
-            std = self.cfg.sigma * torch.sqrt(t * (1.0 - t))[:, None, None]
+        if cfg.sigma > 0:
+            std = cfg.sigma * torch.sqrt(t * (1.0 - t))[:, None, None]
             x_t_cont = x_t_cont + std * torch.randn_like(x_t_cont)
         target_cont = x_1_cont - x_0_cont
-        u = torch.rand(B, T, self.cfg.n_disc_pkt, device=device)
-        keep = u < t[:, None, None]
-        rand_disc = torch.randint(0, self.cfg.n_disc_classes, packets_disc.shape, device=device)
-        x_disc_t = torch.where(keep, packets_disc, rand_disc)
-        disc_start = 1 + self.cfg.n_cont_pkt
-        x_t_full = x_t_cont.clone()
-        x_t_full[:, 1:, disc_start:disc_start + self.cfg.n_disc_pkt] = self._embed_disc(x_disc_t)
+
+        # Disc corruption schedule (mask-pred): keep fraction t of true labels
+        if T > 0 and self.eff_n_disc > 0:
+            u = torch.rand(B, T, self.eff_n_disc, device=device)
+            keep = u < t[:, None, None]
+            rand_disc = torch.randint(0, cfg.n_disc_classes, eff_disc_int.shape, device=device)
+            x_disc_t = torch.where(keep, eff_disc_int, rand_disc)
+            disc_start = (1 if cfg.use_flow_token else 0) + 0  # placeholder; overwritten below
+            # Where in x_t_full do disc embeds go?
+            # Within each packet token: [type(1) | cont(eff_n_cont) | disc(eff_n_disc) | pad...]
+            disc_start_in_token = 1 + self.eff_n_cont
+            cur_offset = 1 if cfg.use_flow_token else 0
+            x_t_full = x_t_cont.clone()
+            x_t_full[:, cur_offset:cur_offset + T, disc_start_in_token:disc_start_in_token + self.eff_n_disc] = self._embed_disc(x_disc_t)
+        else:
+            x_t_full = x_t_cont
+            x_disc_t = eff_disc_int  # unused
+            keep = None
+
         (v_pred, d_logits) = self.velocity(x_t_full, t, key_padding_mask=kpm)
+
+        # CFM regression loss on cont slots (mask out disc slots)
         v_err = (v_pred - target_cont).square()
-        v_err[:, :, disc_start:disc_start + self.cfg.n_disc_pkt] = 0.0
+        if T > 0 and self.eff_n_disc > 0:
+            disc_start_in_token = 1 + self.eff_n_cont
+            cur_offset = 1 if cfg.use_flow_token else 0
+            v_err[:, cur_offset:cur_offset + T, disc_start_in_token:disc_start_in_token + self.eff_n_disc] = 0.0
         v_per_token = v_err.mean(dim=-1)
         per_sample = (v_per_token * mask).sum(dim=-1) / mask.sum(dim=-1).clamp_min(1.0)
         L_cont = per_sample.mean()
-        pkt_logits = d_logits[:, 1:]
-        pkt_real = mask[:, 1:].bool()
-        corrupt = ~keep & pkt_real[:, :, None]
-        flat_logits = pkt_logits.reshape(-1, self.cfg.n_disc_classes)
-        flat_targets = packets_disc.reshape(-1).long()
-        flat_ce = F.cross_entropy(flat_logits, flat_targets, reduction='none')
-        flat_ce = flat_ce.view(B, T, self.cfg.n_disc_pkt)
-        flat_ce = flat_ce * corrupt.float()
-        denom = corrupt.float().sum().clamp_min(1.0)
-        L_disc = flat_ce.sum() / denom
-        total = L_cont + self.cfg.lambda_disc * L_disc
+
+        # Mask-pred CE on corrupted disc positions
+        if T > 0 and self.eff_n_disc > 0 and keep is not None:
+            cur_offset = 1 if cfg.use_flow_token else 0
+            pkt_logits = d_logits[:, cur_offset:cur_offset + T]
+            pkt_real = mask[:, cur_offset:cur_offset + T].bool()
+            corrupt = ~keep & pkt_real[:, :, None]
+            flat_logits = pkt_logits.reshape(-1, cfg.n_disc_classes)
+            flat_targets = eff_disc_int.reshape(-1).long()
+            flat_ce = F.cross_entropy(flat_logits, flat_targets, reduction='none')
+            flat_ce = flat_ce.view(B, T, self.eff_n_disc)
+            flat_ce = flat_ce * corrupt.float()
+            denom = corrupt.float().sum().clamp_min(1.0)
+            L_disc = flat_ce.sum() / denom
+        else:
+            L_disc = L_cont.new_zeros(())
+
+        total = L_cont + cfg.lambda_disc * L_disc
         if return_components:
-            return {'total': total, 'main': L_cont.detach(), 'aux_disc': L_disc.detach(), 'aux_flow': L_cont.new_zeros(()), 'aux_packet': L_cont.new_zeros(())}
+            return {'total': total, 'main': L_cont.detach(), 'aux_disc': L_disc.detach(),
+                    'aux_flow': L_cont.new_zeros(()), 'aux_packet': L_cont.new_zeros(())}
         return total
 
+    # ------------------------------------------------------------------ #
+    # Scoring                                                            #
+    # ------------------------------------------------------------------ #
     @torch.no_grad()
-    def trajectory_metrics(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, n_steps: int=16) -> dict[str, torch.Tensor]:
-        z = self.build_tokens(flow, packets_cont, packets_disc)
+    def trajectory_metrics(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, n_steps: int=16, cont_bin_edges: torch.Tensor | None=None) -> dict[str, torch.Tensor]:
+        cfg = self.cfg
+        B = flow.shape[0]
+        T = self.eff_T
+
+        # Build effective cont / disc tensors per ablation mode
+        if cfg.disc_as_cont:
+            disc_float = self._embed_disc(packets_disc) if T > 0 else None
+            if T > 0:
+                eff_cont = torch.cat([packets_cont, disc_float], dim=-1) if cfg.n_cont_pkt > 0 else disc_float
+            else:
+                eff_cont = packets_cont.new_zeros((B, 0, 0))
+            eff_disc_int = torch.zeros((B, T, 0), dtype=torch.long, device=flow.device)
+        elif cfg.cont_as_disc:
+            if T > 0:
+                if cont_bin_edges is None:
+                    raise ValueError('cont_as_disc requires cont_bin_edges at scoring time')
+                cont_int = self.quantize_cont(packets_cont, cont_bin_edges)
+                eff_disc_int = torch.cat([cont_int, packets_disc.long()], dim=-1)
+            else:
+                eff_disc_int = torch.zeros((B, 0, 0), dtype=torch.long, device=flow.device)
+            eff_cont = flow.new_zeros((B, T, 0))
+        else:
+            eff_cont = packets_cont if T > 0 else packets_cont.new_zeros((B, 0, cfg.n_cont_pkt))
+            eff_disc_int = packets_disc.long() if T > 0 else torch.zeros((B, 0, cfg.n_disc_pkt), dtype=torch.long, device=flow.device)
+
+        z = self.build_tokens(flow, eff_cont, eff_disc_int)
         mask = self._loss_mask(lens)
         kpm = mask == 0
-        B = z.shape[0]
         dt = 1.0 / n_steps
-        disc_start = 1 + self.cfg.n_cont_pkt
-        disc_end = disc_start + self.cfg.n_disc_pkt
-        disc_embed = z[:, 1:, disc_start:disc_end].clone()
+
+        # Disc embed slot bounds (within token vector) for "freeze disc during ODE"
+        cur_offset = 1 if cfg.use_flow_token else 0
+        disc_start_in_token = 1 + self.eff_n_cont
+        disc_end_in_token = disc_start_in_token + self.eff_n_disc
+        if self.eff_n_disc > 0 and T > 0:
+            disc_embed = z[:, cur_offset:cur_offset + T, disc_start_in_token:disc_end_in_token].clone()
+        else:
+            disc_embed = None
+
         for k in range(n_steps):
             t_val = 1.0 - k * dt
             t = torch.full((B,), t_val, device=z.device)
             (v, _) = self.velocity(z, t, key_padding_mask=kpm)
-            v[:, :, disc_start:disc_end] = 0.0
+            if self.eff_n_disc > 0 and T > 0:
+                v[:, cur_offset:cur_offset + T, disc_start_in_token:disc_end_in_token] = 0.0
             z = z - v * dt
-            z[:, 1:, disc_start:disc_end] = disc_embed
+            if disc_embed is not None:
+                z[:, cur_offset:cur_offset + T, disc_start_in_token:disc_end_in_token] = disc_embed
+
+        # Compute terminal-norm scores. Zero out the discrete embed slots so they don't pollute.
         z_real = z * mask[:, :, None]
         z_cont = z_real.clone()
-        z_cont[:, 1:, disc_start:disc_end] = 0.0
-        packet_count = mask[:, 1:].sum(dim=-1).clamp_min(1.0)
-        terminal = z_cont.reshape(B, -1).norm(dim=-1) / (mask.sum(dim=-1) * self.token_dim).clamp_min(1.0).sqrt()
-        terminal_flow = z_cont[:, 0].norm(dim=-1) / math.sqrt(self.token_dim)
-        terminal_packet = (z_cont[:, 1:] * mask[:, 1:, None]).reshape(B, -1).norm(dim=-1) / (packet_count * self.token_dim).sqrt()
-        return {'terminal_norm': terminal, 'terminal_flow': terminal_flow, 'terminal_packet': terminal_packet}
+        if self.eff_n_disc > 0 and T > 0:
+            z_cont[:, cur_offset:cur_offset + T, disc_start_in_token:disc_end_in_token] = 0.0
+
+        full_norm = z_cont.reshape(B, -1).norm(dim=-1) / (mask.sum(dim=-1) * self.token_dim).clamp_min(1.0).sqrt()
+        out = {'terminal_norm': full_norm}
+        if cfg.use_flow_token:
+            out['terminal_flow'] = z_cont[:, 0].norm(dim=-1) / math.sqrt(self.token_dim)
+        if T > 0:
+            packet_count = mask[:, cur_offset:cur_offset + T].sum(dim=-1).clamp_min(1.0)
+            out['terminal_packet'] = (z_cont[:, cur_offset:cur_offset + T] * mask[:, cur_offset:cur_offset + T, None]).reshape(B, -1).norm(dim=-1) / (packet_count * self.token_dim).sqrt()
+        return out
 
     @torch.no_grad()
-    def disc_nll_score(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5) -> dict[str, torch.Tensor]:
-        (B, T, _) = packets_cont.shape
-        device = packets_cont.device
+    def disc_nll_score(self, flow: torch.Tensor, packets_cont: torch.Tensor, packets_disc: torch.Tensor, lens: torch.Tensor, t_eval: float=0.5, cont_bin_edges: torch.Tensor | None=None) -> dict[str, torch.Tensor]:
+        cfg = self.cfg
+        B = flow.shape[0]
+        T = self.eff_T
+        device = flow.device
+        if T == 0 or self.eff_n_disc == 0:
+            return {}  # no disc head to score
+
+        # Build effective disc int per mode
+        if cfg.cont_as_disc:
+            if cont_bin_edges is None:
+                raise ValueError('cont_as_disc requires cont_bin_edges at scoring time')
+            cont_int = self.quantize_cont(packets_cont, cont_bin_edges)
+            eff_disc_int = torch.cat([cont_int, packets_disc.long()], dim=-1)
+            eff_cont = flow.new_zeros((B, T, 0))
+            ch_idx_list = list(cfg.cont_pkt_idx) + list(cfg.disc_pkt_idx)
+        else:
+            eff_disc_int = packets_disc.long()
+            eff_cont = packets_cont
+            ch_idx_list = list(cfg.disc_pkt_idx)
+
         mask = self._loss_mask(lens)
         kpm = mask == 0
-        z = self.build_tokens(flow, packets_cont, packets_disc)
+        z = self.build_tokens(flow, eff_cont, eff_disc_int)
         t = torch.full((B,), float(t_eval), device=device)
         (_, d_logits) = self.velocity(z, t, key_padding_mask=kpm)
-        pkt_logits = d_logits[:, 1:]
-        flat_logits = pkt_logits.reshape(-1, self.cfg.n_disc_classes)
-        flat_targets = packets_disc.reshape(-1).long()
+        cur_offset = 1 if cfg.use_flow_token else 0
+        pkt_logits = d_logits[:, cur_offset:cur_offset + T]
+        flat_logits = pkt_logits.reshape(-1, cfg.n_disc_classes)
+        flat_targets = eff_disc_int.reshape(-1).long()
         ce = F.cross_entropy(flat_logits, flat_targets, reduction='none')
-        ce = ce.view(B, T, self.cfg.n_disc_pkt)
-        pkt_real = mask[:, 1:].bool().float()
+        ce = ce.view(B, T, self.eff_n_disc)
+        pkt_real = mask[:, cur_offset:cur_offset + T].bool().float()
         per_sample = (ce.sum(dim=-1) * pkt_real).sum(dim=-1) / pkt_real.sum(dim=-1).clamp_min(1.0)
         per_ch = (ce * pkt_real[:, :, None]).sum(dim=1) / pkt_real.sum(dim=1).clamp_min(1.0)[:, None]
         out = {'disc_nll_total': per_sample}
-        for (c, idx) in enumerate(self.cfg.disc_pkt_idx):
+        for c, idx in enumerate(ch_idx_list):
             out[f'disc_nll_ch{idx}'] = per_ch[:, c]
         return out
 

Mixed_CFM/train.py

@@ -21,7 +21,7 @@ def _device(arg: str) -> torch.device:
         return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     return torch.device(arg)
 
-def _batch_score(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int) -> dict[str, np.ndarray]:
+def _batch_score(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray, disc_np: np.ndarray, len_np: np.ndarray, device: torch.device, *, batch_size: int, n_steps: int, cont_bin_edges: torch.Tensor | None = None) -> dict[str, np.ndarray]:
     out: dict[str, list[np.ndarray]] = {}
     model.eval()
     for start in range(0, len(flow_np), batch_size):
@@ -30,14 +30,14 @@ def _batch_score(model: MixedTokenCFM, flow_np: np.ndarray, cont_np: np.ndarray,
         cont = torch.from_numpy(cont_np[sl]).float().to(device)
         disc = torch.from_numpy(disc_np[sl]).long().to(device)
         lens = torch.from_numpy(len_np[sl]).long().to(device)
-        m = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps)
-        d = model.disc_nll_score(flow, cont, disc, lens)
+        m = model.trajectory_metrics(flow, cont, disc, lens, n_steps=n_steps, cont_bin_edges=cont_bin_edges)
+        d = model.disc_nll_score(flow, cont, disc, lens, cont_bin_edges=cont_bin_edges)
         for src in (m, d):
             for (k, v) in src.items():
                 out.setdefault(k, []).append(v.detach().cpu().numpy())
     return {k: np.concatenate(v, axis=0) for (k, v) in out.items()}
 
-def _quick_eval(model: MixedTokenCFM, data: MixedData, device: torch.device, cfg: dict[str, Any]) -> dict[str, float]:
+def _quick_eval(model: MixedTokenCFM, data: MixedData, device: torch.device, cfg: dict[str, Any], cont_bin_edges: torch.Tensor | None = None) -> dict[str, float]:
     n_eval = int(cfg.get('eval_n', 2000))
     rng = np.random.default_rng(0)
 
@@ -46,8 +46,8 @@ def _quick_eval(model: MixedTokenCFM, data: MixedData, device: torch.device, cfg
         return rng.choice(n, m, replace=False)
     vi = pick(len(data.val_flow))
     ai = pick(len(data.attack_flow))
-    v = _batch_score(model, data.val_flow[vi], data.val_cont[vi], data.val_disc[vi], data.val_len[vi], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8)))
-    a = _batch_score(model, data.attack_flow[ai], data.attack_cont[ai], data.attack_disc[ai], data.attack_len[ai], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8)))
+    v = _batch_score(model, data.val_flow[vi], data.val_cont[vi], data.val_disc[vi], data.val_len[vi], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8)), cont_bin_edges=cont_bin_edges)
+    a = _batch_score(model, data.attack_flow[ai], data.attack_cont[ai], data.attack_disc[ai], data.attack_len[ai], device, batch_size=int(cfg.get('eval_batch_size', 512)), n_steps=int(cfg.get('eval_n_steps', 8)), cont_bin_edges=cont_bin_edges)
     y = np.concatenate([np.zeros(len(vi)), np.ones(len(ai))])
     out: dict[str, float] = {}
     for k in sorted(v.keys()):
@@ -73,9 +73,36 @@ def train(cfg: dict[str, Any]) -> Path:
     ds = TensorDataset(torch.from_numpy(tr_f).float(), torch.from_numpy(tr_c).float(), torch.from_numpy(tr_d).long(), torch.from_numpy(tr_l).long())
     loader = DataLoader(ds, batch_size=int(cfg['batch_size']), shuffle=True, drop_last=True, num_workers=int(cfg.get('num_workers', 0)), pin_memory=device.type == 'cuda')
     print(f'[data] training on {len(ds):,} flows')
-    model_cfg = MixedCFMConfig(T=data.T, flow_dim=data.flow_dim, token_dim=cfg.get('token_dim'), d_model=int(cfg['d_model']), n_layers=int(cfg['n_layers']), n_heads=int(cfg['n_heads']), mlp_ratio=float(cfg.get('mlp_ratio', 4.0)), time_dim=int(cfg.get('time_dim', 64)), sigma=float(cfg.get('sigma', 0.1)), use_ot=bool(cfg.get('use_ot', False)), reference_mode=cfg.get('reference_mode'), lambda_disc=float(cfg.get('lambda_disc', 1.0)))
+    n_disc_classes = int(cfg.get('n_disc_classes', 2))
+    model_cfg = MixedCFMConfig(
+        T=data.T, flow_dim=data.flow_dim, token_dim=cfg.get('token_dim'),
+        d_model=int(cfg['d_model']), n_layers=int(cfg['n_layers']), n_heads=int(cfg['n_heads']),
+        mlp_ratio=float(cfg.get('mlp_ratio', 4.0)), time_dim=int(cfg.get('time_dim', 64)),
+        sigma=float(cfg.get('sigma', 0.1)), use_ot=bool(cfg.get('use_ot', False)),
+        reference_mode=cfg.get('reference_mode'), lambda_disc=float(cfg.get('lambda_disc', 1.0)),
+        n_disc_classes=n_disc_classes,
+        # B-group ablation flags
+        use_flow_token=bool(cfg.get('use_flow_token', True)),
+        n_packet_tokens=int(cfg.get('n_packet_tokens', -1)),
+        disc_as_cont=bool(cfg.get('disc_as_cont', False)),
+        cont_as_disc=bool(cfg.get('cont_as_disc', False)),
+    )
     model = MixedTokenCFM(model_cfg).to(device)
-    print(f'[model] params={model.param_count():,} token_dim={model.token_dim} sigma={model_cfg.sigma} use_ot={model_cfg.use_ot} lambda_disc={model_cfg.lambda_disc}')
+    # B4: compute bin edges from benign train cont (z-scored, masked) for cont_as_disc quantization
+    cont_bin_edges = None
+    if model_cfg.cont_as_disc:
+        n_bins = n_disc_classes
+        n_cont_orig = model_cfg.n_cont_pkt
+        # gather real cont samples per channel (mask padding)
+        masks = np.arange(data.train_cont.shape[1])[None, :] < data.train_len[:, None]
+        edges = np.zeros((n_cont_orig, n_bins - 1), dtype=np.float32)
+        for c in range(n_cont_orig):
+            vals = data.train_cont[..., c][masks]
+            qs = np.linspace(0, 1, n_bins + 1)[1:-1]  # interior quantiles
+            edges[c] = np.quantile(vals, qs).astype(np.float32)
+        cont_bin_edges = torch.from_numpy(edges).to(device)
+        print(f'[B4] cont_bin_edges shape={tuple(edges.shape)}  (n_bins={n_bins})')
+    print(f'[model] params={model.param_count():,} token_dim={model.token_dim} sigma={model_cfg.sigma} use_ot={model_cfg.use_ot} lambda_disc={model_cfg.lambda_disc} use_flow_token={model_cfg.use_flow_token} n_packet_tokens={model_cfg.n_packet_tokens} disc_as_cont={model_cfg.disc_as_cont} cont_as_disc={model_cfg.cont_as_disc}')
     opt = torch.optim.AdamW(model.parameters(), lr=float(cfg['lr']), weight_decay=float(cfg.get('weight_decay', 0.01)))
     total_steps = max(1, int(cfg['epochs']) * len(loader))
     sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=total_steps)
@@ -91,7 +118,7 @@ def train(cfg: dict[str, Any]) -> Path:
             cont = cont.to(device, non_blocking=True)
             disc = disc.to(device, non_blocking=True)
             lens = lens.to(device, non_blocking=True)
-            comp = model.compute_loss(flow, cont, disc, lens, return_components=True)
+            comp = model.compute_loss(flow, cont, disc, lens, return_components=True, cont_bin_edges=cont_bin_edges)
             loss = comp['total']
             ldisc_sum += float(comp['aux_disc'].item())
             opt.zero_grad(set_to_none=True)
@@ -104,7 +131,7 @@ def train(cfg: dict[str, Any]) -> Path:
         mean_loss = float(np.mean(losses)) if losses else float('nan')
         eval_metrics: dict[str, float] | None = None
         if epoch % int(cfg.get('eval_every', 5)) == 0 or epoch == int(cfg['epochs']):
-            eval_metrics = _quick_eval(model, data, device, cfg)
+            eval_metrics = _quick_eval(model, data, device, cfg, cont_bin_edges=cont_bin_edges)
         history['epoch'].append(epoch)
         history['loss'].append(mean_loss)
         history['eval'].append(eval_metrics)
@@ -120,6 +147,8 @@ def train(cfg: dict[str, Any]) -> Path:
         if not np.isfinite(mean_loss):
             raise RuntimeError(f'non-finite loss at epoch {epoch}')
     payload = {'model_state_dict': model.state_dict(), 'model_cfg': asdict(model_cfg), 'cont_mean': data.cont_mean, 'cont_std': data.cont_std, 'flow_mean': data.flow_mean, 'flow_std': data.flow_std, 'flow_feature_names': np.asarray(data.flow_feature_names), 'packet_feature_names': np.asarray(data.packet_feature_names)}
+    if cont_bin_edges is not None:
+        payload['cont_bin_edges'] = cont_bin_edges.detach().cpu().numpy()
     torch.save(payload, save_dir / 'model.pt')
     with open(save_dir / 'history.json', 'w') as f:
         json.dump(history, f, indent=2, default=str)