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All Journal Journal of Technology Informatics and Engineering International Journal of Graphic Design
Yuxuan Ren
Chemical Engineering & Data Science, University of Washington, WA, USA
Arts Computer Science & IT

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Layout-Aware Progressive PDF Rendering: AI Prioritization of PDF Slices to Reduce Time-to-Functional-First-Frame on FUNSD Heyu Wang; Yuxuan Ren; Xiaohan Chang
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.523

Abstract

Progressive PDF rendering is attractive because users rarely need every visible pixel at once; they need the semantically useful parts of the current viewport early enough for reading and interaction. This paper studies whether layout-aware AI can prioritize PDF slices more effectively than geometric or density-based heuristics. We reconstruct vector PDFs from official FUNSD form annotations and evaluate a tile scheduler that predicts tile utility from inexpensive layout and preview features before high-resolution rendering begins. The empirical study covers 26 reconstructed documents from the FUNSD test split that were fully processed in the present environment, four viewport scenarios, and measured clip-render timings for all visible tiles. The main configuration uses an 8×10 grid and a random-forest regressor trained with page-level 5-fold GroupKFold, then compares the learned scheduler with row-major visible-first, center-first, ink-density, text-density, a hand-tuned layout heuristic, full-page rendering, and an oracle upper bound. The proposed model reaches TTFF-90 in 14.21 ms, compared with 15.18 ms for the best non-AI heuristic, 20.48 ms for full-page rendering, and 24.09 ms for row-major rendering. It also achieves Utility@20ms of 0.941, AUC@25ms of 0.730, NDCG@10 of 0.963, and Recall@10 of 0.969. The results show that slice rendering is not inherently beneficial: the summed visible-tile cost in the main 8×10 setting is 28.80 ms, which is higher than the full-page cost of 20.48 ms, so scheduling quality determines whether slicing improves or harms TTFF. A coarser 6×8 grid reduces AI TTFF-90 to 10.58 ms, while the densest pages favor a full-page fallback. Paired Wilcoxon signed-rank tests over the page-scenario cases yield p < .001 for TTFF-90 improvements of the proposed model over every non-AI baseline. However, those tests should be interpreted as case-level rather than document-level evidence.
Grounded AI Pointer Cards for Browser Agents: Evidence-Aware Web Element Reranking and Action Explanation on WebLINX Yinchen Shi; Yuxuan Ren
International Journal of Graphic Design Vol. 4 No. 1 (2026): April | IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v4i1.4002

Abstract

AI browser agents increasingly promise to turn the mouse pointer into a situated interface for selecting page content, asking follow-up questions, scrolling to relevant sections, and opening contextual panels. For graphic design research, this shift matters because the pointer card is both a computational output and a visual communication device: it must make the target, supporting evidence, and next action legible at the moment of use. This paper evaluates evidence-aware web element reranking as the grounding layer for AI pointer cards. The empirical study uses the WebLINX candidate-reranking data, including validation, same-distribution test, and unseen-website test partitions. Each query is paired with candidate DOM/page elements and binary relevance labels. Nine reranking methods were evaluated, ranging from random and DOM-salience controls to BM25 variants, a transparent hybrid formula, a logistic reranker, and a depth-limited decision-tree reranker. Across 5,883 queries and 1,556,655 query–element pairs, the tree reranker achieved the highest same-distribution nDCG@10 (0.272) and Recall@10 (0.473), while the fixed hybrid achieved the highest unseen-website Top-1 accuracy (0.104). Combining lexical and DOM evidence improved ranking, whereas layout features transferred less consistently across websites. Because the first-ranked target was correct in only 10.4% of unseen-website queries, the findings support a confirm-before-act grounding prototype with visible alternatives and correction controls rather than autonomous execution. The reported metrics evaluate target ranking rather than card usability.
Co-Authors Heyu Wang Xiaohan Chang Yinchen Shi