Visualization of Machine Learning Models through Their Spatial and Temporal Listeners

Abstract

Model visualization (ModelVis) has emerged as a major research direction, yet existing taxonomies are largely organized by data or tasks, making it difficult to treat models as first-class analysis objects. We present a model-centric two-stage framework that employs abstract listeners to capture spatial and temporal model behaviors, and then connects the translated model behavior data to the classical InfoVis pipeline. To apply the framework at scale, we build a retrieval-augmented human--large language model (LLM) extraction workflow and curate a corpus of 128 VIS/VAST ModelVis papers with 331 coded figures. Our analysis shows a dominant result-centric priority on visualizing model outcomes, quantitative/nominal data type, statistical charts, and performance evaluation. Citation-weighted trends further indicate that less frequent model-mechanism-oriented studies have disproportionately high impact while are less investigated recently. Overall, the framework is a general approach for comparing existing ModelVis systems and guiding possible future designs.

Figures (6)

• Figure 1: Overview of our model-centric ModelVis framework. (A) A model listener layer captures model evidence from input data, training configuration, model structure, learnable parameters, transient state, dynamics (time), and output results, and quantifies these observations into analysis-ready data. (B) The quantified outputs are then mapped to the classical InfoVis pipeline, including data organization, transformation, visualization, and interaction.
• Figure 2: Annual distribution of the 136 identified ModelVis papers in our VIS/VAST corpus. The red line shows the number of papers per publication year, highlighting a marked increase after 2016 (11-16 papers/year).
• Figure 3: Summarizing the sampling of the 136 identified ModelVis papers. Papers are first grouped by identification source (human-labeled, $n$ = 35; LLM-labeled, $n$ = 101) and then by whether they were selected for Stage 3 figure-level annotation ($n$ = 46) or not ($n$ = 90).
• Figure 4: A visualization of parallel sets of 1000 ModelVis paths at figure-level for the final 331-figure corpus on model listener$\rightarrow$data type$\rightarrow$visualization type$\rightarrow$visualization purpose. Node heights and link widths indicate the relative prevalence of labels and cross-stage transitions. The dominant routes are output-oriented listening through quantitative/nominal representations to statistical chart and then to performance evaluation, with secondary flows toward I/O relationship and other visualization types.
• Figure 5: Yearly category proportions in the 128-paper ModelVis corpus (2010---2024) across the four framework labels. Charts A--D correspond to the model listener, data type, visualization type, and visualization purpose, respectively. Each curve reports the within-year proportion of papers assigned to a category, showing sustained dominance of output-results listening and performance-oriented analysis, with a stronger presence of mechanism-related categories after 2017.