# On Semiotic-Grounded Interpretive Evaluation of Generative Art

Source: [On Semiotic-Grounded Interpretive Evaluation of Generative Art](<https://www.sota2.com/research/paper/69dc788d7d0172140ec78ad6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-16T06:00:01.807Z

Authors: Ruixiang Jiang, Changwen Chen

Published: 2026-04-09T17:30:10.000Z

## Abstract

Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface-level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human-GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes - iconic, symbolic, and indexical - yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning-making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation-intensive fine-art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of "pretty" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semiosis Quality Evaluation on HGI SemiosisArt](<https://www.sota2.com/research/sota/semiosis-quality-evaluation-on-hgi-semiosisart.md>): Semiosis Quality Evaluation; HGI SemiosisArt; KRCC: 0.746.

- [Visual Question Answering on HGI SemiosisArt](<https://www.sota2.com/research/sota/visual-question-answering-on-hgi-semiosisart.md>): Visual Question Answering; HGI SemiosisArt; Accuracy: 92.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.08641>)

[PDF](<https://arxiv.org/pdf/2604.08641.pdf>)

[Implementation](<https://github.com/songrise/SemJudge>)