LLM2Jev
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An architecture-preserving framework that extracts calibrated decisions directly from next-token probabilities over bracketed numeric identifiers, providing a training-free inference recipe and a fine-tuning objective (tree-factorized listwise loss with KL divergence penalties anchoring auxiliary pr
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