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.
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...
The paper introduces SCX Router, a lightweight GLiClass-based model selector that assigns suitability scores to inference-time language models without autoregressive generation. It uses a 0.6B-parameter Qwen3 decoder with a shallow bidirectional scorer, preserving a text-only key–value cache across sessions and predicting task attributes such as type, difficulty, and expected output length. The authors build a comprehensive task ontology with 23 families, 115 types, and 1,173 synthetic examples, generating 150,000 verifier-scored tasks to train the router, which outperforms baseline models on LiveBench subsets with a top‑1 score of 0.707 versus 0.696 for the strongest fixed model.
arXiv:2609.23886v1 Announce Type: new Abstract: Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears wit...
The paper evaluates Certo, a small non‑generative decision model that scores candidate actions based on text. It compares a joint scorer that processes state, rules, and candidates together with a cacheable encoder that pre‑encodes candidates to reduce cost. Experiments show the cacheable approach loses rule sensitivity, while targeted counterfactual supervision can recover performance on synthetic tasks; however, on real rules the joint scorer still outperforms the cacheable version, and cross‑domain mixtures do not improve accuracy.
The study examines how renaming option labels in typed decision models affects model behavior. By swapping the names of two options (e.g., from 0/1 to no/yes) while keeping the underlying rubrics unchanged, the authors observed a dramatic shift in decision rankings—AUC dropped from .94 to .23 and answer flips increased by 70.4 per hundred. The effect is amplified with more options and depends on the semantic polarity of the labels, yet the models still maintain a zero type‑error rate.
Typed decision models are built for settings where model outputs are consumed directly by software. Instead of generating free-form text, they return a decision over a predefined set of options. By co...
The paper introduces Jev, a reinforcement‑learning‑trained model that provides calibrated probability answers to typed questions about a single input in one call. Jev is evaluated on RLCDAlignBench, a benchmark covering ten alignment failures across 44 tests and five target models, achieving a median AUROC of 0.886 zero‑shot and outperforming supervised baselines on most tasks. The study shows that question wording has little impact, while contextual fields that encode labels are more influential, and that Jev matches human‑label agreement while being 63× cheaper than LLM‑judge scorers.
arXiv:2609.39496v1 Announce Type: cross Abstract: Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making workflows by selecting directly from predefine...
arXiv:2608. 15565v1 Announce Type: new Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide.
The paper audits a commercial non‑generative System‑1 model on biosecurity‑relevant benchmarks, evaluating accuracy, calibration, error detection, selective prediction, and sensitivity to answer‑option order. It finds that the model’s accuracy varies strongly by task, is reasonably well calibrated when the vendor’s uncertainty field is interpreted correctly, and that option order can cause significant prediction changes—averaging across rotations improves accuracy. The study also shows that applying averaging only to low‑confidence items recovers most of the gain at a lower cost.
The paper introduces a signed lexical gate that combines a sentence classifier’s logit margin with a sparse lexical model’s support for the predicted intent, assigning positive evidence to lexical agreement and negative evidence to a lexically favored competing intent. This gate retains more information than unsigned lexical confidence or a hard agreement rule and is calibrated via an independent binomial procedure to meet specified risk targets. Experiments on BANKING77, CLINC150, and HWU64 show that the proposed score reduces the area under the risk‑coverage curve by up to 15.8% and increases accepted coverage at low error rates, offering a compact, interpretable confidence enhancement for risk‑calibrated intent routing.
The study investigates whether Jev, a typed classifier that outputs probabilities over allowed answers without generating text, can replace large language model (LLM) rubric judges. Across nine panels from seven benchmarks, Jev’s accuracy differed significantly from LLM judges in only 8 of 27 paired comparisons, performing best on binary criteria and worse only on graded ones, while most other comparisons were inconclusive. In terms of cost and speed, Jev was 29 to 325 times cheaper and 30 to 220 times faster than the flash‑tier LLM judges, and a cascade approach that defers uncertain Jev verdicts to an LLM yielded only modest gains. whyItMatters":"The findings suggest that a lightweight classifier like Jev can serve as an efficient first‑stage evaluator, potentially reducing the reliance on expensive and slow LLM judges in automated grading pipelines."