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
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...
LAVOIR is a single‑pass decision encoder that not only predicts answers to typed questions but also identifies which missing pieces of information (slots) would most improve its confidence. By placing candidate slots next to answer options, one forward pass yields both the decision distribution and the expected value of asking each slot, without requiring human labels. In controlled experiments, LAVOIR’s question policy matches a greedy oracle and improves accuracy by up to 14.1 points over never asking, while on real conversations it raises accuracy by 8.3 points with minimal questioning.
By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay
arXiv:2608. 15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question.
By Volodymyr Ovcharov
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
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:2610.02586v1 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
JevOut demonstrates that natural, short additions to the context of decision models can flip their outputs from correct to incorrect, even when the correct answer remains unchanged. By optimizing context additions while keeping the source, question, choices, and gold answer fixed, the study found that 61.4% of initially correct decisions were redirected to a wrong option, with 45% receiving high confidence. Similar fragility was observed across three other decision systems on seven datasets, with flip rates between 64.9% and 73.2%.
By Zixiang Xu
The paper introduces a paired candidate‑coverage benchmark protocol for typed decision models, evaluating two models—Laya and Jev—on datasets such as AG News, DBpedia, Emotion, and TREC. It reports that Laya detects a high percentage of missing-answer cases but also falsely rejects many valid candidates, whereas Jev shows lower false rejection rates but also lower detection of missing answers. The study highlights the need for separate measurements of classification, score ranking, and rejection policies, noting that the benchmark is descriptive and limited to reference‑label omission.
By Jiawen Lu, Tongtong Wu
The paper investigates whether confidence scores from a black-box decision model, Jev, truly reflect missing knowledge. Using over 15 public datasets and 6 synthetic task families, the authors find that while Jev’s confidence is calibrated on familiar closed-choice tasks, it fails to indicate when the model lacks relevant information—assigning high confidence to salient options even without answer-relevant data and overestimating accuracy on news beyond its knowledge boundary. Targeted yes/no questions about whether an outcome is settled or whether evidence suffices provide sharper indicators of knowledge gaps, but only when surface cues are controlled.
By Sharath M Shankaranarayana, Davor Runje, Jan Jannink
The paper compares a non‑generative verifier (Jev) with a frontier LLM (Astra) on 495 human‑labelled claims from LLM‑AggreFact. Both models achieve statistically indistinguishable accuracy, yet Jev is 187‑fold cheaper and admits more unsupported claims. A confidence‑gated cascade that escalates uncertain claims improves cost‑efficiency, though the benefit comes mainly from the escalation share rather than the probability estimates.
By Alden Do Rosario, Kirollos Atef
arXiv:2610.29052v1 Announce Type: cross
Abstract: A typed decision maps evidence, a question and a fixed set of options to a probability for every option, computed in one forward pass, by reading a l...
By Yuchuan Weng, Chi Liu, Xin Chen, Srinivasan Manoharan