TabAgent: Distilling LLM Agent Components into Cheap Classifiers
TabAgent is proposed, a framework for replacing generative decision components in closed-set selection tasks with a compact textual-tabular classifier trained on execution traces, establishing a paradigm for learned discriminative replacements of generative bottlenecks in production agent architectures.
Ido Levy, Segev Shlomov, Avi Yaeli et al.
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