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THE DAILY INDEX

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GENERAL

TLDR Dev

LANGUAGE MODELS FOR TEXT CLASSIFICATION: FROM BAG-OF-WORDS TO JEV

This visual history traces text classification from bag-of-words and recurrent networks through transformers and Jev-style decision APIs. Jev is positioned between narrow specialist classifiers and general-purpose language models, offering broad zero-shot classification with lower latency and cost. The practical opportunity is to use calibrated decision models for routing, screening, evaluation, and other bounded choices inside agent systems.

JEV AND THE RETURN OF THE CLASSIFIERS

Jev returns typed choices, scores, or yes-or-no probabilities instead of generating prose, making bounded decisions fast and inexpensive. Its “zero hallucination” claim means outputs always fit the supplied schema, not that every decision is correct. The more important promise is calibrated confidence that can route uncertain cases to humans or slower reasoning models, though that calibration requires validation on real workloads.

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