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LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a typed input state, a question, and a set of options, they return a probability for each option in a single forward pass, without text generation. We show that existing ope...
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diffusion-augmented LLMs, a new class of models that defines an AR model distribution while using diffusion...
ADAPT---Amortized Distillation Across Post-Trained LLMs is introduced, a framework for amortizing distillation across both axes of a model family: size and post-training variant, producing models for interpolated sizes across post-trained variants with a single distillation run.
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