# When the Right Answer Is Missing: An Arithmetic-Dependent Rejection Bottleneck in Jev

arXiv: https://arxiv.org/abs/2609.39496 · HTML: https://mlsanity.com/paper/2609.39496/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions](https://mlsanity.com/paper/2506.09038/) (2025)
- [Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models](https://mlsanity.com/paper/2407.16221/) (2024)
- [LLMs May Perform MCQA by Selecting the Least Incorrect Option](https://mlsanity.com/paper/2402.01349/) (2024)
- [Type-Safe Is Not Error-Free: Typed Decision Models Follow the Option Name, Not the Definition Bound to It](https://mlsanity.com/paper/2609.26758/) (2026)
- [Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding](https://mlsanity.com/paper/2609.27678/) (2026)
- [None of the Above, Less of the Right: Parallel Patterns between Humans and LLMs on Multi-Choice Questions Answering](https://mlsanity.com/paper/2503.01550/) (2025)

## Built on by (most cited first, 2 of 2)

- [Benchmarking candidate coverage and rejection policy transfer in typed decision models](https://mlsanity.com/paper/2610.03387/) (2026)
- [The Company a Question Keeps: Auditing Open Jev-Style Models for Retrieval](https://mlsanity.com/paper/2610.14631/) (2026)

## Abstract (arXiv)

Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making workflows by selecting directly from predefined options. When candidate sets contain no valid answer, TypeSafe recommends including an "other" or "none-of-the-above" option to enable rejection. In this report, however, we identify an arithmetic-dependent rejection bottleneck: Jev reliably selects correct numerical answers when available but frequently accepts incorrect alternatives when they are absent despite an explicit rejection option. On paired arithmetic problems, answer-present accuracy reaches 99%, while correct rejection falls to 7%. Moreover, this gap persists across numerical magnitudes, operation depths, contextual formulations, and rejection labels, and extends to scenarios such as time calculation and capacity rounding. Yet native Boolean verification achieves 99% exact-match accuracy on the same answer-absent arithmetic cases, showing that categorical rejection can fail even when the model successfully verifies candidate correctness. Finally, we show that a simple decision threshold selected on separate development problems raises arithmetic rejection accuracy from 7% to 79% while retaining 97% answer-present accuracy, substantially mitigating the failure without retraining or additional inference.