Banger report on foundation decision models.

This work presents a 27B open decision model that lands within 1.6 points of Claude Opus 5.5 on Decision Bench v4 and answers in 183 ms instead of 3.5s.

Nace AI released Drex, a family of four open decision models at 8B, 9B, 35B with 3B active, and 27B.

You give the model a state, such as a document, a record, or an agent's context, plus questions with a fixed set of options. It returns a probability for each option without generating text.

The 27B Max model scores 67.52 on the public Decision Index 0.4.1 suite, against 60.70 for Jev 1.14.0.

On Decision Bench v4, it is statistically tied with GPT-6 Sol and d3-turbo. It also has the best accuracy among decision models on DecideBench.

Model Training:

After fine-tuning, training adds reinforcement learning from adjusted feedback (RLAF). A teacher model proposes a probability for each option, and the verified answer adjusts it, so the model trains on calibrated targets in place of one-hot labels. RLAF adds 2.0 Decision Index points, and a consistency loss lowers the rate of flipped answers when the options are reordered from 6.3% to 4.2%.

HF: https://huggingface.co/nace-ai/drex-max-v1

Paper: https://arxiv.org/abs/2611.07854

Chat with Paper: https://academy.dair.ai/papers/towards-foundation-decision-models-drex-2611.07854