Papers by Haohao Luo
INREACT: An Inspire-Then-Reinforce Training Framework For Multimodal GUI Agent (2025.findings-emnlp)
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| Challenge: | Existing multimodal large language models struggle with precise localization of small elements. |
| Approach: | They propose a multimodal GUI agent framework that unifies observing, thinking, and acting for precise and interpretable decision-making. |
| Outcome: | The proposed framework unifies observing, thinking, and acting for precise and interpretable decision-making. |
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation (2024.acl-long)
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| Challenge: | Existing methods for multiple choice questions focus on text inputs and lack visual information. |
| Approach: | They propose a framework to generate subject-specific educational questions with plausible distractors based on multimodal content. |
| Outcome: | The proposed framework improves question generation and distractor generation over existing methods across subjects and educational levels. |
Express What You See: Can Multimodal LLMs Decode Visual Ciphers with Intuitive Semiosis Comprehension? (2025.findings-acl)
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| Challenge: | Traditional VQA benchmarks encounter a modality gap and over-reliance on language priors, whereas human cognition excels at intuitive semiosis, associating abstract visual symbols to linguistic semantics. |
| Approach: | They propose a task of generating abstract linguistics from emoji sequence images, where such reasoning underpins critical applications in cryptography. |
| Outcome: | The proposed model can generate abstract linguistics from emoji sequence images, challenging MLLMs’ reasoning of decoding complex semantics of visual ciphers. |
Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are sensitive to the contextual position of information in input. |
| Approach: | They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. |
| Outcome: | Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures. |
Unifying Text, Tables, and Images for Multimodal Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to multimodal question answering rely on single-modal or bi-modal models, which limit their ability to integrate information across all modalities. |
| Approach: | They propose a framework that unifies three different input modalities into a text-to-text format by employing position-enhanced table linearization and diversified image captioning techniques. |
| Outcome: | The proposed framework unifies three input modalities into a text-to-text format using position-enhanced table linearization and diversified image captioning techniques. |
Browsing Like Human: A Multimodal Web Agent with Experiential Fast-and-Slow Thinking (2025.acl-long)
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| Challenge: | Existing web agents lack visual perception, planning, and memory abilities, but their reasoning process is deviate from human cognition. |
| Approach: | They propose a multimodal web agent framework that emulates human planning process to decompose complex user instructions. |
| Outcome: | The proposed framework emulates human planning process to decompose complex user instructions. |