ResearchResearch paperReinforcement Learning · Large Language Models · Reasoning & Planning1 source · Oct 18, 2026

What Can't Jev Decide? Probing the Limits of One-Pass Decision Models

Jev and its open replicas choose among the options a caller supplies with a single forward pass, fast enough to decide inside an agent loop.

We ask what such a model cannot decide, and find a simple limit: Jev cannot make a decision that an LLM makes only by writing out intermediate steps.
On a benchmark that varies one factor at a time, Jev finds and selects records without error up to 20k tokens and 64 options, but once the answer depends on two or more updates of one user, accuracy falls, to 53-80% at sixteen updates, because similar records interfere.
On real decision items graded by how many thinking tokens a reference LLM needs, Jev answers 97.5% of the items that one forward pass of that LLM solves, but only 74% of the others.
The open models we evaluate share these limits.

[1]What Can't Jev Decide? Probing the Limits of One-Pass Decision Models
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 12:50 AM
Jev and its open replicas choose among the options a caller supplies with a single forward pass, fast enough to decide inside an agent loop.
We ask what such a model cannot decide, and find a simple limit: Jev cannot make a decision that an LLM makes only by writing out intermediate steps.

Extractive summary: sentences quoted from the sources.

Before this