The paper introduces Drex, a family of four open-source decision models (8B, 9B, 35B with 3B active, and 27B) that predict probability distributions over user-supplied options without autoregressive decoding. These models are trained with supervised fine‑tuning, reinforcement learning from adjusted feedback, and, for the Max variant, vision tuning, achieving state‑of‑the‑art accuracy on Decision Index 0.4.1 and strong performance on Decision Bench v4 while delivering faster, more consistent responses than larger GPT‑style models.
By Nischay Dhankhar, Alisher Amantay, Zhanibek Datbayev, Darkhan Akhmetov, Yash Shah, Nurmukhamed Ubaidullayev, Raman Saparkhan, Dos Baha, Abulhair Saparov
arXiv:2609.37647v1 Announce Type: cross
Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
By Tobias Deu{\ss}er, Lorenz Sparrenberg, Rafet Sifa
EDU-Jev is a family of single‑pass decision models (0.8B, 2B, and 4B parameters) designed for educational tasks that require selecting from a fixed set of options. The models output a probability distribution over predefined choices without generating text, using a pointer head to score each option. In twelve education benchmarks, EDU‑Jev 4B achieved 67.6% accuracy, outperforming five commercial decision APIs by 9.4 percentage points and the best open 4B model by 18.7 points, while the 2B version can process about 150 decisions per second on a single H100 GPU.
By Jiazheng Li, Yulan He
Intern-Decision is a family of multimodal decision models (0.8B, 2B, and 4B parameters) that output structured choices and candidate probabilities directly from text and optional images, avoiding the latency of free‑form responses. The models use an autoregressive masked language modeling objective, placing one decision placeholder per field in a complete assistant JSON skeleton, and evaluate all fields in a single causal forward pass. The 4B variant achieves 90.02 % unweighted average accuracy across seven evaluation suites, with a mean latency of 44.16 ms on an RTX 4090 for a 289‑token request, and shows improved calibration and accuracy when combined with temperature scaling and confidence‑based handoff.
By Tong Zhu, Zhe Xu, Zefeng He, Yunkai Chai, Xuyang Hu, Dehong Zheng, Yun Luo, Yafu Li, Qi Zhang, Yu Cheng
The paper investigates how compact an encoder can be for closed‑set decision tasks in language‑model pipelines, comparing joint versus per‑option scoring. In a 32‑million‑parameter study, per‑option scoring outperforms joint scoring by 5.8 chance‑corrected skill points on a 37‑benchmark suite, and two external benchmarks corroborate this advantage. Across six released joint encoders ranging from 17 million to 1 billion parameters, the 68‑ and 150‑million‑parameter models surpass a 435‑million‑parameter DeBERTa‑v3 zero‑shot classifier on 209 overlapping tasks and match it on 145 unseen tasks, while the smallest model processes a median request in 14 ms versus 728 ms for the classifier.
By Tardelli Ronan Coelho Stekel
Intern-Decision introduces 0.8B, 2B, and 4B multimodal decision models that output structured choices and probabilities directly from text and optional images, avoiding the latency of free‑form responses. The models use an autoregressive masked language modeling objective, placing decision placeholders in a JSON skeleton and evaluating all fields in a single causal pass. The 4B variant attains 90.02% unweighted accuracy across seven benchmarks, with sub‑45 ms latency on an RTX 4090, and demonstrates improved calibration and handoff performance in pilot studies.
arXiv:2608. 20106v1 Announce Type: new Abstract: We introduce OenoBench, a wine-domain knowledge benchmark of 3,266 multiple-choice questions across six pillars (regions, grape varieties, viticulture, winemaking, producers, business) and four difficulty tiers.
By Nikita Khudov
Prism-1 introduces Position- and Rival-Independent Scoring for System One models, ensuring that option ordering and set changes do not affect decision probabilities. It achieves this by reading each option in its own block at shared positions, preserving softmax independence across options. Experiments on Qwen3-8B and Gemma 4 12B show Prism-1 maintains baseline accuracy while eliminating sensitivity to shuffling and irrelevant options.
By Yao Long Teng, Jianguo Huang, Yanchen Deng, Jiayi Cai, Bo An
Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass, without generated reasoning. We...
arXiv:2607. 25136v1 Announce Type: new Abstract: Research on preference optimization often varies the training objective while holding the data fixed.
By Zhengtao Yao, Runhao Li, Xupeng Chen, Jiayi Cheng, Chenqian Le, Michael Yue, Siheng Wang, Haoyan Xu, Yuqi Li, Chenhao Wei, Zhengdao Li, Rongchao Zhang, Guang Yang, Yidong Wang, Junhao Dong
LEV is a decision model that performs reasoning by looping its layers instead of generating text, allowing it to deepen its computation on a per-question basis. Trained jointly across all loop steps, a single LEV checkpoint can answer at any depth, improving accuracy—especially on hard questions—while keeping inference fast. On benchmark tests, LEV outperforms dense and cascade models by 5.4 to 8.8 accuracy points with sub‑50 ms latency, and on‑demand depth selection saves 15–24% of computation for minimal accuracy loss.
By Dingjie Song, Zhiling Yan, Yuxuan Zhang, Lichao Sun
arXiv:2610.08452v1 Announce Type: cross
Abstract: Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring...
By Lasse B. Strand, Robert Jakob, Kevin O'Sullivan, Markus Kreft