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# System One models

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  1. arXiv

    Option order stops steering the answer: iterated cyclic reordering drives flip rate to zero across five benchmarks and five models

    System One decision models answer a choice question in one forward pass, but the answer shifts with the order in which options are listed, and the probabilities carry a prior over answer labels. IJev starts from an order that ignores the input order, repeatedly reorders options by a prior-corrected cyclic average, and averages the round distributions instead of returning the last round. Across five benchmarks and five models, every cell has flip rate zero; the default uniform round average lowers negative log-likelihood in 24 of 25 cells while accuracy stays comparable to a single cyclic average.

    Chinese Academy of Sciences
    Youzhi Zhang（First author, Corresponding author）
    IJevSystem One modelsPosition Bias
  2. arXiv

    One character changed, the decision changed: 30 text models and 17 vision models take the same attack candidates

    DecisionRobust evaluates JEV-style decision models under text and image perturbations and prompt injection, and finds that 30 text models and 17 vision models take the same candidate set, with held-out models excluded from candidate selection also incurring errors while label-consistency checks sharply reduce apparent attack success.

    Pennsylvania State University
    Yifan Lan（First author）
    DecisionRobustAdversarial RobustnessPrompt InjectionSystem One models
  3. arXiv

    Cheaper decisions, not always faster tasks: five agents and 4,500 runs draw the line

    Swapping agent action selection for a fast decision model like Jev cut per-decision cost by 66.1% to 78.4% and decision time by 73.9% to 81.5%, yet task success held near baseline on ALFWorld and fell more sharply in two mobile agents, so local savings do not automatically become delivered outcomes.

    Nanjing UniversityTechnical University of Munich
    Yuchen Ling（First author）
    JevSystem One modelsAgent evaluationLLM AgentsALFWorld
  4. arXiv

    Retrieval encoders retooled for one-pass decisions: prior-fused heads hold up with scarce labels, cross heads win on full data

    Biomedical retrieval sentence encoders can be converted into typed decision models that return thresholdable probabilities in one forward pass, with a cross head reaching 66–73 seen-task points, close to fine-tuned Laya-large at 75.0 with about three times the parameters.

    Queen's University Belfast
    Pritam Deka（First author, Corresponding author）
    sbert2s1System One modelsBioDecideMEDLINE-S1RLCD
  5. arXiv

    Porcedda's Sys1Cal-v1 shows Jev's Choice probabilities are systematically distorted, and restoring an uncertainty component lifts median soft accuracy from 0.771 to 0.978

    Porcedda built Sys1Cal-v1, a synthetic dataset of 365 True/False items derived from 92 probability problems whose proposition probabilities are known by construction, and used total variation distance and distributional overlap (soft accuracy) to evaluate Jev's Noul, Choice, and Score primitives plus the open-source baseline SemIf, finding that Jev's Choice probabilities are systematically distorted and that inverting a latent third truth value of uncertainty estimated from Score expectations raises median Choice soft accuracy from 0.771 to 0.978.

    Scuola Superiore Sant'AnnaUniversity of Pisa
    Riccardo Porcedda（First author, Corresponding author）
    Sys1Cal-v1Probability CalibrationJevSystem One models
  6. arXiv

    Fudan team trains Chinese-Jev on 10 million Chinese decisions, reaching 69.20% general accuracy and roughly 20x faster than Jev

    The work introduces Chinese-Jev: a unified data pipeline converts heterogeneous Chinese annotations into probability targets over candidate options, a lightweight encoder-only model is first pre-trained on 10 million general Chinese decisions and then fine-tuned separately for medicine, law, and finance, and CJ-Bench is released with 307,900 held-out decisions; after pre-training it reaches 69.20% accuracy on general tasks, a 1.24% relative gain over the closed-source Jev, cuts expected calibration error from 11.45% to 3.78% at 14 ms latency, improves medical accuracy over Jev by 4.0%, reaches 92% of Jev's average accuracy across the three specialized domains at 15 ms average latency, and an INT8-quantized model runs about 1 second per decision in a mobile browser.

    Chinese-JevCJ-BenchSystem One modelsRLCD

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