The report introduces AnyJev, a method that extracts typed decisions from a pretrained instruction‑tuned language model by restricting the next‑token distribution to a set of option tokens. It corrects two defects—label bias and position bias—without any gradient updates by dividing out a label prior and averaging log‑probabilities over cyclic rotations of the option list. Experiments on two 20‑option tasks show that rotations reduce order‑flip rates and improve accuracy across all tested models, while a stopping rule reduces the number of required rotations and increases decision throughput.
By Jiamu Zhang, Tianze Yang, Yucheng Shi, Evan Chen, Zixiang Nie, Kelly Wan, Liangjie Hong, Ninghao Liu, Liang Wu
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 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.
By Yu Sun, Junhao Xu, Jiajia Shi, Zijin Yang
arXiv:2610.02586v1 Announce Type: new
Abstract: A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carri...
By Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram
Prefill‑only decision models evaluate every candidate in a menu in a single forward pass, avoiding decoding and reducing cost by one to two orders of magnitude compared to generative language models. The paper demonstrates that when only the candidate menu changes, the model’s post‑intervention accuracy can be predicted solely from the cached first‑pass distribution using a simple estimator that renormalizes and selects the argmax, without any labels or second pass. Across seven model families, ten datasets, and two task types, this menu‑only intervention prediction is within 4.2 points of actual accuracy, and in one family it is exact, whereas a probability‑level variant fails by 21 points, indicating the property resides in ranking rather than calibrated probabilities.
By Ran Li, Lei Chen
NumericJev introduces a training‑free numerical decoding algorithm that allows large language models with Jev‑like structured‑choice interfaces to output precise numerical values. The method refines a numerical range using a multiway decision tree, achieving lower mean absolute error than direct selection from a candidate list. Experiments on an arithmetic benchmark show a 2.93 percentage‑point improvement, and a historical‑index study reports a 4.58% mean relative recall error with zero readout error when the value is supplied.
By Weiwei Ye, Hangchen Liu, Renhe Jiang
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...
By Zehua Cheng, Wei Dai, Jiahao Sun
The paper investigates whether an off‑the‑shelf System‑One large language model (LLM) can perform knowledge tracing (KT) when few or no learners are logged. On seven datasets, the model—named Jev—achieves a mean AUC of .706 without any target platform data, outperforming 28 deep KT models trained on eight learners and a System‑Two Thinking‑KT approach. Adding a few logged examples and a similar‑learner statistic (JevKT) further improves performance to .722, maintaining superiority over deep KT up to 64 learners and surpassing supervised KT until 128 learners.
By Unggi Lee, Haeun Park
The paper demonstrates that typed decision models can produce type‑correct outputs that nevertheless do not adhere to the explicit definitions of their options. By swapping only the names of options while keeping the definitions, question, and state unchanged, the authors show that decision flips can increase dramatically—up to 70.4 percentage points—when using yes/no names versus neutral controls. This effect is consistent across multiple models and decision rules, and even random strings as names yield flip rates similar to neutral controls, indicating that option‑name polarity contributes to decision instability beyond mere reassignment.
By Yu Sun, Junhao Xu, Jiajia Shi, Zijin Yang
The paper corrects earlier claims about how system prompts affect hidden‑state geometry in open‑weight language models. After auditing the code, the authors withdraw regime‑specific and direction‑to‑magnitude claims, but find that centroid distance, maximum mean discrepancy, and a linear probe still distinguish prompt conditions across all models. Curvature statistics only exceed noise in a minority of comparisons, and in Gemma‑4‑E4B‑it the identity prompt yields a lower‑norm state than a token‑matched generic prompt, aligning with observed pause behavior in outputs.
By Jorge Castillo Sep\'ulveda, Marco Torres Y\'evenes, Juan Carlos Lanas
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
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