← Wednesday, October 7, 2026AgentsPaper

LLMs can make typed decisions directly from next-token probabilities

huggingface.co by Yinheng Li, Justin Wagle Editor score 8/10

LLM-as-Jev reads calibrated choices from probabilities over numeric identifiers and adds a listwise fine-tuning objective; the untrained Qwen3.5-4B matches community models on the same backbone and supports multimodal decisions. This provides a direct decision interface for agent workflows without free-form generation.

What

- LLM-as-Jev investigates LLMs as decision models that return categorical probability distributions over predefined options.

Method

- Its framework extracts decisions from next-token probabilities over bracketed numeric IDs; fine-tuning uses listwise loss and KL penalties.

Why it matters

- The authors find strong models can decide without training, while targeted fine-tuning helps weaker models and tasks without degrading conversational behavior.

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