Compiling agentic workflows into LLM weights creates subterranean agents with near-frontier quality at two orders of magnitude less cost, validated empirically on travel booking, Zoom support, and insurance claims tasks.
Advances in Neural Information Processing Systems , volume=
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TabSHAP attributes feature impact in LLM tabular classifiers via sampled Shapley coalitions and JSD on output distributions, reporting higher deletion faithfulness than random or XGBoost-proxy baselines on Adult Income and Heart Disease data.
citing papers explorer
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Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost
Compiling agentic workflows into LLM weights creates subterranean agents with near-frontier quality at two orders of magnitude less cost, validated empirically on travel booking, Zoom support, and insurance claims tasks.
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TabSHAP
TabSHAP attributes feature impact in LLM tabular classifiers via sampled Shapley coalitions and JSD on output distributions, reporting higher deletion faithfulness than random or XGBoost-proxy baselines on Adult Income and Heart Disease data.