UI2App introduces a benchmark showing that vision-language models can reconstruct web page visuals but largely fail to infer the underlying interaction logic from screenshots alone.
C hart I nsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
3 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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Pith papers citing it
9
external citations · OpenAlex
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2026 3representative citing papers
Attentive-CoT is an attention-guided fine-tuning objective that improves chain-of-thought performance in multimodal LLMs by delaying answer commitment and increasing sustained visual-token access during rationale generation.
PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.
citing papers explorer
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UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
UI2App introduces a benchmark showing that vision-language models can reconstruct web page visuals but largely fail to infer the underlying interaction logic from screenshots alone.
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Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
Attentive-CoT is an attention-guided fine-tuning objective that improves chain-of-thought performance in multimodal LLMs by delaying answer commitment and increasing sustained visual-token access during rationale generation.
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Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
PolyChartQA is a new mid-scale dataset for multi-chart question answering that reveals a 27.4% accuracy drop for multimodal models on human-authored questions compared to AI-generated ones, plus a modest gain from a proposed prompting method.