Speaking of decision models, one interesting family just came out - Drex by @NaceAI. This family includes 4 open-source decision models, each of which skips text generation altogether and directly assigns probabilities to the available options. Drex explores 4 different ways to do this: 1. Three smaller models use a pointer architecture: It consists of a pretrained LM + a small head that compares option representations with the question. They process the context once and reuse it to answer multiple questions. This saves time and computation. 2. The largest, Drex Max, is a dense multimodal decision model It reprocesses the context for each question, using bidirectional attention to compare all (up to 255) options together before assigning probabilities. That costs more computation but gives better accuracy. Training is interesting too. Drex learns from labeled decisions across 11 domains: first supervised fine-tuning, then reinforcement learning from adjusted feedback (RLAF) that mixes verified answers with a teacher model’s probability estimates to calibrate confidence. Drex Max adds an extra vision-training stage. And a few interesting results which show that it works: - Speed: Drex Max takes 183 ms vs. 2s for GPT-6 Sol and 3.5s for Claude Opus 5.5. - Accuracy: Beats Jev 1.14 on several benchmarks and approaches frontier LLMs, though Claude Opus 5.5 still leads on Decision Bench v4. - Consistency: RLAF improved its benchmark score by 2 points, while consistency training cut answer flips from 6.3% to 4.2% when options were reordered.