[Submitted on 13 Oct 2026]
Title:DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision
View a PDF of the paper titled DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision, by Yifan Lan and 6 other authors
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Abstract:Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As these models are increasingly used for automated decision-making, understanding their reliability under adversarial manipulation becomes critical. We introduce DecisionRobust, a benchmark for systematically evaluating the robustness of decision models across textual and visual inputs. Our benchmark covers practical decision-making scenarios and a diverse suite of attacks, including semantics-preserving text perturbations, image perturbations, and prompt injection in both modalities. We curate evaluation sets through external validity checks and cross-model candidate selection. We evaluate 30 model configurations on text tasks and 17 on vision tasks, including 24 and 12 held-out models, respectively, that are excluded from candidate selection. Our experiments reveal several key findings. First, attacks expose vulnerabilities beyond the models used for construction and selection, although optimized image perturbations transfer weakly to many targets even with multiple surrogates. Second, relative model robustness varies across tasks and attack types, and resistance to input perturbations does not necessarily imply resistance to prompt injection. Third, label-consistency checks substantially reduce measured attack success across text and image settings, highlighting the importance of validating apparent failures. The benchmark is available at this https URL.
Comments: 21 pages, 6 figures, including references and appendices. Benchmark website: this https URL
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.19118 [cs.CL]
(or arXiv:2610.19118v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.19118
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yifan Lan [view email] [v1] Tue, 13 Oct 2026 19:52:14 UTC (9,532 KB)
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