Skip to content

Type-Safe Is Not Error-Free: A Constrained Decision Head Follows the Option Name, Not the Rubric Bound to It

Sep 2026 · 3 citations · 17 references
Computer Science

TL;DR

Jev and two Jev-like models with open weights are studied by changing how option names are assigned to rubrics, revealing a systematic reversal in the decision ranking rather than simple uncertainty.

Abstract

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 construction, every output conforms to the required schema. Yet this guarantee does not tell us whether the model interprets the options as intended. We study Jev and two Jev-like models with open weights by changing how option names are assigned to rubrics. Each option consists of an option name and a textual rubric that defines what the option means. We change only which option name is assigned to each rubric; the question, state, rubric wording, and set of option names remain exactly the same. On 1200 workflow decisions with task-specific rubrics, renaming the two options from 0/1 to no/yes changes 70.4 more answers per hundred (95% CI: [67.6, 73.1]) and shifts AUC from .94 to .23, revealing a systematic reversal in the decision ranking rather than simple uncertainty. The same operation has little effect with neutral option names. This pattern holds across all 4 predicates, where the effect is at least 7.4x larger than under the neutral control, and becomes stronger as the number of options increases. The effect also depends on the read-out geometry: a second model family that mean-pools over the full option span flips 4.1x less often. The hosted model exhibits the same behavior: the swap changes AUC from .8146 to .5806 and produces 24x as many answer flips as its test-retest floor. In contrast, replacing the option names with random character strings returns all model families to the neutral-control regime without reducing accuracy. The failure therefore depends on the semantic polarity of the option names rather than on the renaming operation itself. Across all conditions, the type-error rate remains 0%, even when decision accuracy degrades substantially.

View source

Similar papers

Preprint Sep 2026

Type-Safe Is Not Error-Free: Typed Decision Models Follow the Option Name, Not the Definition Bound to It

Typed decision models return structured results, but output-type correctness alone does not ensure that decisions follow explicit option definitions. Each option pairs a name with a definition that defines its intended meaning; the name, however, can provide a competing semantic cue. We study this conflict in Jev and t...

Yu Sun, Jun-Hao Xu, Jia-Jia Shi et al. · 24 citations · ⚡2
#natural language process... Preprint Sep 2026

this-that-model-1.0: A typed decision model that decides in 30 ms, for a millionth of a cent

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 without a person. What the program needs back is not prose. It is one of n declared options and a number it can threshold. Today that costs a round trip to a frontier model...

Ze-Hua Cheng, Wei Dai, Jia-Hao Sun · 6 citations · ⚡2
#artificial intelligence Preprint Oct 2026

Labels Override Definitions in Jev-Style Typed Decision Models

A typed decision model answers a fixed question about an input by returning a probability for each of several caller-defined options. Each option carries a short label and a written definition, which is where a developer states the rule the model should apply. Jev introduced this interface for routing, moderation and t...

Seyedarmin Azizi, Erfan Baghaei Potraghloo, M. Pedram · 1 citation
#artificial intelligence Preprint Sep 2026

Evaluating and Benchmarking the System One Model Jev

Jev answers MMLU's calculation-heavy questions more accurately than other MMLU questions (94% vs. 91%), whereas both open models, and all three on C-Eval, find them harder.

Tobias Deußer, L. Sparrenberg, R. Sifa · 9 citations · ⚡1
Review Open access Sep 2026

Automatic Model Selection Based on Task Complexity

An Adaptive Intelligence Router is set out that chooses among deterministic programs, retrieval and service interfaces, small language models, more capable language models, more capable language models, and accountable human review.

L. Santhanam · 0 citations
Preprint Aug 2026

Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL

This semantic path compilation (SPC) system against direct DDL-to-SQL generation on the ACME insurance benchmark supports an end-to-end systems result, not a causal claim that compilation alone produced the gain, because SPC receives governed semantic artifacts that the DDL baseline does not.

Yi Ai · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.