Lost in the Fallback: How Reliably Do Typed Decision Models Use Fallback Options?

Title:Lost in the Fallback: How Reliably Do Typed Decision Models Use Fallback Options?

Abstract:Typed decision models (TDMs) are increasingly used in applications such as request routing and model selection, making reliable decisions critical to system performance. Fallback options accommodate categories outside regular choices, enabling deferral to human experts or more capable models. While recent works have examined adversarial vulnerabilities in TDMs, their ability to follow fallback rules in non-adversarial settings remains underexplored. We introduce the Paired Fallback Robustness (PFR) Test to evaluate two aspects of fallback reliability: whether models correctly switch between regular and fallback options, and whether they make consistent decisions under logically equivalent definitions of the same rule. We evaluate open-source decision models on three benchmarks, with controlled comparisons across architectures and parameter counts. We find that correct recognition does not reliably transfer to correct fallback options, and errors persist even when the correct category is explicitly this http URL, logically equivalent formulations of the same fallback rule can also produce substantial disagreement in model decisions. These findings reveal a crucial reliability gap in current TDMs: fallback mechanisms designed to improve reliability can instead introduce errors. It highlights faithful execution of fallback rules and consistency under equivalent definitions as essential requirements for reliable typed decision-making.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2610.19856 [cs.CR]

(or arXiv:2610.19856v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2610.19856 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.