Nace AI Open-Sources the Drex Decision Model Family for Single-Pass Option Scoring

Summary:Nace AI has open-sourced Drex, a family of four decision models designed to score a set of candidate options in a single forward pass rather than generating free-form text. The lineup covers a diffusion language model, a Gated DeltaNet hybrid, a sparse mixture-of-experts model, and a dense vision-capable variant. Pointer-style architectures cache the encoded state, allowing multiple questions to reuse the same representation while excluding cross-question attention. Training combines full-param

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Drex Turns Generation into Calibrated Option Selection

Nace AI has open-sourced Drex, a family of models intended to score a set of candidate options rather than generate a free-form answer. A request contains a state—such as a document, database record, or agent context—optionally accompanied by an image, plus a list of labeled choices. Drex returns a normalized probability distribution over those choices in one forward pass, with no autoregressive decoding or output-string parsing.

That interface is useful for agent routing, tool selection, policy decisions, and classification. The relevant quality measures extend beyond Top-1 accuracy: probabilities should be calibrated, predictions should remain stable under option reordering, and the encoded state should be reusable across questions.

Four Architectures, Different Deployment Trade-offs

Drex DLM (8B) uses a diffusion language model with bidirectional attention over the context. Diffusion models update sequence representations iteratively rather than decoding one token at a time, potentially reducing decoding dependencies. Their effective latency, however, still depends on the number of denoising steps and the serving implementation.

Drex v1.5 (9B) uses a Gated DeltaNet hybrid. DeltaNet-style linear attention recursively updates a compact state, reducing the compute and memory cost of long-context attention. The trade-off is possible information loss from state compression; hybrid attention is commonly used to restore finer local modeling.

Drex v2.0 is a sparse mixture-of-experts model with 35B total parameters and 3B active parameters. Only selected experts run for each token, but actual throughput depends on routing stability, batching efficiency, expert-parallel communication, and kernel support. Active parameter count alone does not predict production performance.

Drex Max is a dense 27B model with an image encoder. It reads each question in full and applies a linear readout over option codes. Dense execution is generally easier to optimize and deploy, but it also incurs higher compute and memory-bandwidth requirements than a similarly sized sparse MoE.

Pointer Caching Separates State Encoding from Question Scoring

Drex DLM, v1.5, and v2.0 use a pointer decision head. The model encodes the state once and caches it; each subsequent question attends to that cached state and scores the available options, while questions are prevented from attending to one another.

This design avoids recomputing a long document prefix when one state produces many decisions and reduces cross-question leakage. The corresponding limitation is fixed-capacity compression: every question reads the same cached representation, making the architecture less suitable when a question depends on the result of an earlier question.

Drex Max takes the opposite route. It fully processes each question and performs a linear readout over option encodings. This suits questions containing complex instructions but forfeits the state-reuse advantage of the pointer architecture.

Consistency Training Targets Decision-Specific Failure Modes

Nace AI performed full-parameter supervised fine-tuning on 1.15 million verified decision records containing approximately 2.9 million questions, then applied consistency objectives and Reinforcement Learning from Adjusted Feedback, or RLAF.

The consistency objective shuffles option order or the state and penalizes changes in the resulting prediction. Nace reports that the option-order flip rate fell from 6.3% to 4.2%. For routing and tool-selection models, this is more important than aggregate accuracy alone: the ordering of candidates should not become an implicit answer feature.

RLAF uses verified outcomes and judge feedback to adjust the model. Nace reports a 2.0-point Decision Index gain, a 1.5-percentage-point accuracy improvement, and a 0.9-percentage-point reduction in calibration error over SFT. The summary does not isolate the contribution of verified rewards versus judge feedback or disclose full training cost.

Benchmark Comparisons Need Careful Interpretation

Drex Max scores 67.52 on the public Decision Index 0.4.1 suite, compared with 60.70 for Jev 1.14.0. Drex v2.0 reports an expected calibration error of 1.37% across approximately 115,000 held-out decisions. Nace also reports a typical Drex Max latency of 183 milliseconds, versus approximately 2.0 seconds for GPT-6 Sol and 3.5 seconds for Claude Opus 5.5.

These numbers do not establish that Drex is universally an order of magnitude faster. A decision model scores a bounded option set, whereas a chat model generates open-ended text; output length, prompts, hardware, service load, and timing methodology may differ. A stronger comparison would include end-to-end latency, cost per million decisions, concurrent throughput, tail latency, quantization settings, and cache behavior.

Drex also trails frontier chat models on closed-book knowledge. That does not invalidate its routing role, but it defines the model as a specialized policy head over a restricted action space rather than a general knowledge model. The report acknowledges that same-backbone ablations have not yet been completed, preventing clean attribution of performance differences among the four architectures.

Deployment Recommendations

Official releases include Safetensors and GGUF weights, with llama.cpp and Ollama support. Drex v1.5 and v2.0 support contexts up to 131K tokens, while Drex Max supports up to 262K.

Local deployments should test three priorities:

1. Quantization impact: Compare FP16/BF16, Q8, Q6, Q5, and Q4 using decision accuracy, ECE, and option-flip rate rather than perplexity alone.
2. Cache economics: Pointer models fit workloads where one state triggers many questions. Measure real cache reuse and avoid paying the full memory cost of long contexts when batching cannot amortize it.
3. Production calibration: Use task-specific held-out data for temperature scaling or isotonic calibration. Monitor ECE, flip rate, and overconfidence by class. Probabilities should not be treated as execution thresholds without domain calibration.

For agent systems, the safest integration pattern is to use Drex for candidate tool or route selection while retaining a general-purpose model for open-ended generation. High-risk actions should remain subject to human confirmation or independent policy checks.

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Source: Reddit r/LocalLLaMA (2026-11-06)

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Tags: #Drex Follow #Decision Models Follow #Sparse MoE Follow #Gated DeltaNet Follow #Agent Routing Follow