QVAC Genesis III is a 191.43 B‑token synthetic STEM corpus covering 19 domains and multiple difficulty levels, created through a dual generation strategy that uses a weak edge‑scale student model to generate corrective explanations and contrastive reasoning. The authors evaluate the corpus with an LLM‑as‑a‑parser protocol and demonstrate that 1.7 B‑parameter models trained on QVAC Genesis III outperform those trained on Cosmopedia‑v2 and the Cosmo‑1B model on ARC, GPQA Diamond, and MMLU STEM benchmarks, achieving up to +28.57% improvement on ARC‑E and a 99.45% valid answer rate.
By Davide Vitabile, N. Ranjan, Akshay Nambiar, Kamal K. Gupta, Amril Nazir
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
arXiv:2610.15501v1 Announce Type: new
Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does...
By Ziyi Chen, Yan Zhang, Jianhui Wei, Jianing Yin, Daoan Zhang
arXiv:2610.17205v1 Announce Type: new
Abstract: LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a ty...
By Jonathan Geuter, Andrew Cheng, David Alvarez-Melis
TypedBench is a new benchmark designed to evaluate system‑one decision models that output calibrated probabilities for typed answers—categorical, ordinal, or binary—using a non‑generative interface. The benchmark assesses models on policy adherence, wording robustness, probability quality (via selective prediction, ordinal proper scoring rules, and calibration error), and the cost of decisions under asymmetric cost matrices, using seven policy‑labelled generators and nine evaluation suites. Experiments on a hosted model, an open encoder, and a range of open decoders (0.8B–9B parameters) reveal that the hosted model is wording‑sensitive and underconfident, while decoders are slow and less accurate on policy questions, highlighting the need for joint evaluation across multiple dimensions.
By Rahul Sharma, Andrew B. Ducan, Ga\'etan Marceau Caron, Sebastian J. Vollmer
The paper introduces Jev, a specialized decision model that selects among given options rather than generating text, and benchmarks it against 19 large language models (LLMs) across 13 multiple-choice tasks in knowledge, reasoning, and multilingual domains. Jev performs competitively with frontier LLMs on knowledge and commonsense benchmarks, achieving the top score on MMLU-Redux and ARC-Challenge, and surpasses most representative and all small LLMs except in mathematical word problems where it lags behind. The study demonstrates that a specialized decision model can match general-purpose LLMs on knowledge-based decisions but struggles with multi-step calculations.
By Xing Li, Qingcheng Chang, Jinzhong Ning, Changfeng Xu, Shenlong Zhang, Yijia Zhang, Ling Luo, Hongfei Lin
The paper introduces System 1 models—fast, single‑pass decision engines that map inputs directly to structured outputs, addressing the latency, cost, and calibration issues of large language models. It presents a five‑family taxonomy (encoder‑based classifiers, reward models, non‑autoregressive engines, contrastive models, and extraction models) and discusses training, deployment trade‑offs, and calibration theory, including proper scoring rules and RLCD. The guide also covers integration into agentic workflows, System 1+System 2 cascades, 18 application domains, an open‑source ecosystem, and practical resources such as code, a model zoo, and assessment questions.
By Haggai Roitman
The paper introduces Chinese Decision, a family of Chinese-language models designed for single-pass probabilistic decision-making. The models generate logits for candidate choices in one forward pass and normalize them without autoregressive generation, supporting Boolean, categorical, and ordinal decisions. A 0.8B model trained on 13.048 million decisions achieves 72.24% general-domain accuracy and 76.98% average accuracy across medicine, law, and finance, while a 2B model shows comparable performance after partial training.
By Qiang Yan (Institute of Computing Technology, Chinese Academy of Sciences)
The paper evaluates System One decision models—typed models that output probabilities for branching decisions—against supervised classifiers and generative language models on automated decision gate tasks. Eight checkpoints from six families, including the hosted model Jev, were benchmarked on workflow, intent, and social‑science items, showing that small trained classifiers match or slightly outperform decision models on intent and workflow when labels are available, while decision models outperform zero‑shot classifiers when labels are absent. The study also explores calibration, risk thresholds, and cost‑efficiency trade‑offs, providing condition‑dependent design guidelines for automated decision gates.
By Amir Rafe, Subasish Das
Alice is a new large-scale German benchmark for Automatic Short Answer Scoring that focuses on rubric-based evaluation. It contains three subtasks—learning performance (Alice‑LP), knowledge elements (Alice‑KE), and skills (Alice‑SK)—and is designed to assess how well students master underlying concepts and skills rather than just answer questions directly. Experiments show that large language models struggle with zero‑shot scoring of knowledge elements and skills, and that rubric text is especially helpful for Alice‑KE and Alice‑SK.
By Zhifan Sun, Sebastian Gombert, Jannik Lossjew, Tobias Wyrwich, Berrit Katharina Czinczel, David Bednorz, Marcus Kubsch, Knut Neumann, Hendrik Drachsler
arXiv:2610.05107v2 Announce Type: replace-cross
Abstract: Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for...
By Congfeng Cao, Lipeng Zuo, Konstantinos Papakostas, Qiwei Xu, Songwei Xu, Qianru Meng, Lun Zhou, Zhaochun Ren, Yougang Lyu, Xiaohui Yan
arXiv:2609.39111v1 Announce Type: new
Abstract: Human intelligence relies heavily on learned intuition: recognising patterns and judging situations without explicitly unfolding every intermediate ste...
By Li Ding, Haidi Jin, Chen Ji