Scikit-learn
Joblib
English
protocol-routing
llm-routing
multi-agent-systems
cost-aware-inference
protocol-selection
calibration
reasoning
reproducibility
emnlp2026
Instructions to use AgentsSci/EMNLP_Cost-Aware-Protocol-Routing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use AgentsSci/EMNLP_Cost-Aware-Protocol-Routing with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("AgentsSci/EMNLP_Cost-Aware-Protocol-Routing", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 1,283 Bytes
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"selected_config": {
"name": "plain_l2_c0.25",
"class_weight": null,
"C": 0.25
},
"selection_metric": "macro_f1 then avg_excess_cost_tokens_vs_oracle after validation-based early stopping",
"dev_metrics_before_refit": {
"n_examples": 416,
"accuracy": 0.569712,
"macro_f1": 0.267917,
"realized_solve_rate": 0.627404,
"avg_total_tokens": 51731.351,
"avg_model_calls": 15.312,
"avg_wall_time_seconds": 89.666,
"avg_excess_cost_tokens_vs_oracle": 19073.486,
"missed_success_rate": 0.323834
},
"loss_history_epochs": 30,
"early_stopping_metric": "dev_log_loss",
"selected_epoch": 1,
"best_dev_log_loss": 1.177944,
"feature_dimensions": {
"source_cardinality": 63,
"domain_cardinality": 155,
"total_feature_dim": 220
},
"label_counts_train": {
"baseline_llm": 1898,
"single_agent": 768,
"none": 244,
"per": 292,
"broadcast": 140
},
"label_counts_dev": {
"baseline_llm": 237,
"single_agent": 96,
"per": 36,
"none": 30,
"broadcast": 17
},
"label_counts_test": {
"baseline_llm": 238,
"none": 32,
"single_agent": 97,
"broadcast": 18,
"per": 38
},
"features": [
"source",
"domain",
"difficulty",
"difficulty_tier"
]
}
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