arXiv:2609.36965v1 Announce Type: new
Abstract: System One models such as Jev offer an efficient alternative to generative language models for tasks that require decisions rather than open-ended resp...
By Zexiao Wang, Zihao Zhang, Xudong Wang, Pan Wang, Ziyi Ye, Haoyu Zhao, Zuxuan Wu, Shuicheng Yan
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
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
arXiv:2608. 03854v1 Announce Type: new Abstract: When decoder language models are used as classifiers, predicted class probabilities depend on implementation choices, including the prompt template, verbalizer (label-to-token mapping), and scoring rule, that are rarely treated as experimental variables.
By Anton Rasmussen, Hong Qin
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
arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
By Matthew ffrench-Constant, Daniel Yang, Xinmeng Huang, Sanyam Kapoor