Title: Assessing the Usability and Revizability of Scientific Imagery

Abstract:

Scientific imagery serves as essential evidence in research dissemination, but its reliability is increasingly jeopardized by AI-generated content that embeds subtle yet significant inaccuracies. Current assessment methods are insufficient; traditional perceptual metrics fail to align with scientific validity, and language models lack the specialized knowledge required for domain-specific verification. To bridge this divide, we introduce the Scientific Image Utility and Upgradability Assessment (SIU$^2$A) framework. This approach evaluates scientific images along two complementary axes. Utility is defined by two components: error detection, which involves spotting scientific inaccuracies, and correction feasibility, which determines if such errors can be reliably fixed. Upgradability gauges the quality of these corrections.

We classify scientific image corruption into four primary categories: Detail Distortion, Incompleteness, False Content, and Entity Confusion. Leveraging this taxonomy, we developed SIU$^2$A-Benchmark, a dataset annotated by experts to facilitate error identification and repair analysis. The framework employs a two-stage evaluation protocol. The first stage, Utility, assesses the system’s ability to detect errors and generate repair instructions. The second stage, Upgradability, evaluates whether the corrections accurately restore scientific validity while preserving existing correct information. Our experiments highlight substantial shortcomings in current multimodal systems regarding both scientific error assessment and faithful correction, underscoring a critical disconnect between visual perception and scientific applicability.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC