update model card

README.md

@@ -12,34 +12,51 @@ Colab from the private dataset repo `CipherSmit/critiq-v2-data` and served by th
 
 | Folder | Model | Role (tier) | Format |
 |---|---|---|---|
-| `rf/` | Random Forest (scikit-learn) | Tier 2: actor class from 12 features | skops |
+| `seq/` | Trace Transformer: causal Transformer over request tokens, pre-trained to predict the next request | Tier 2: actor class from its recent requests | safetensors |
+| `rf/` | Random Forest (scikit-learn) on 12 hand-made features | baseline classifier (ablation only) | skops |
 | `vae/` | beta-VAE, 60→…→32 latent + aux P99 head | Tier 3: workload fingerprint, OOD scores | safetensors + JSON |
 | `vae_plain/` | same architecture, β = 0, no aux loss | ablation only | safetensors + JSON |
 | `mlp/` | Policy MLP 38→64→32→4 | Tier 5: priority P0–P3 per actor | safetensors |
 
-Training is sequential (RF → beta-VAE → MLP): the VAE's input includes the RF class mix,
-and the MLP's input includes the RF class, RF confidence and the VAE fingerprint.
+Training is sequential (classifier → beta-VAE → MLP): the VAE's input includes the classifier's
+class mix, and the MLP's input includes the class, its confidence and the VAE fingerprint.
 Architectures and feature order live in `critiq-colab/critiq_models/` of the project repository.
 
 ## Intended use and limits
 
 Research use in trace-driven simulation (MSR Cambridge, FIU, Alibaba block traces).
-Not validated on live systems. Labels for the RF come from a hand-built FIU
-process→class map; priority labels come from counterfactual simulation of an
-approximate BFQ scheduler. Results depend on the simulator's fidelity (see the
+Not validated on live systems. Class labels come from transparent behaviour rules over
+actor features (or a hand-built FIU process→class map); priority labels come from
+counterfactual simulation of an approximate mq-deadline scheduler with priority classes. Results depend on the simulator's fidelity (see the
 calibration report in the project's `research/` folder).
 
 ## Metrics
 
+### seq/
+
+| metric | value |
+|---|---|
+| inference_ms_500_actors | 40.9555 |
+| label_source | msr_behavior_rules |
+| latency_critical_recall | 0.8923 |
+| linear_probe_val_macro_f1 | 0.7794 |
+| macro_f1 | 0.9267 |
+| macro_f1_holdout_traces | 0.9249 |
+| model | trace_transformer |
+| parameters | 74345 |
+| train_dataset | msr |
+| val_macro_f1 | 0.9028 |
+
 ### rf/
 
 | metric | value |
 |---|---|
-| inference_ms_500_actors | 28.3510 |
+| inference_ms_500_actors | 37.2847 |
 | label_source | msr_behavior_rules |
 | latency_critical_recall | 0.9997 |
-| macro_f1 | 0.9999 |
-| macro_f1_holdout_traces | 0.9999 |
+| macro_f1 | 0.9997 |
+| macro_f1_holdout_traces | 0.9998 |
+| model | random_forest_baseline |
 | train_dataset | msr |