Papers with large multimodal models
OpenWebAgent: An Open Toolkit to Enable Web Agents on Large Language Models (2024.acl-demos)
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Iat Long Iong, Xiao Liu, Yuxuan Chen, Hanyu Lai, Shuntian Yao, Pengbo Shen, Hao Yu, Yuxiao Dong, Jie Tang
| Challenge: | OpenWebAgent integrates large language models and large multimodal models to improve web automation. |
| Approach: | They propose to integrate large language models and large multimodal models into an open toolkit to optimize web automation. |
| Outcome: | The open toolkit integrates both large language models (LLMs) and large multimodal models (LMMs) it enables the development of powerful, task-oriented web agents, significantly enhancing user experience and operational efficiency on the web. |
MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for multimodal reasoning in large multimodal models are underperforming on multimodal tasks. |
| Approach: | They propose a benchmark for multimodal reasoning in large multimodal models, MM-MATH . MM's process evaluation employs LMM-as-a-judge to automatically analyze solution steps . diagram misinterpretation is the most common error, they find . |
| Outcome: | The proposed model achieves only 31% accuracy, compared to 82% for humans. |
MICE: Mixture of Image Captioning Experts Augmented e-Commerce Product Attribute Value Extraction (2025.acl-industry)
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| Challenge: | Existing visual attribute value extraction methods rely on product images and textual information, which can be ambiguous, inaccurate, or unavailable. |
| Approach: | They propose a framework that leverages a curated pool of image captioning models to generate accurate captions from product images. |
| Outcome: | The proposed framework significantly improves state-of-the-art large multimodal models in zero-shot and fine-tuning settings. |
MMSearch-R1: Incentivizing LMMs to Search (2026.acl-long)
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| Challenge: | Existing approaches to deploying large multimodal models rely on rigid pipelines . Existing methods such as retrieval-augmented generation and prompt engineered search rely only on rigid knowledge sources. |
| Approach: | They propose a framework that enables LMMs to perform on-demand, multi-turn search in real-world Internet environments. |
| Outcome: | The proposed model outperforms existing models while reducing search calls by over 30%. |
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment (2024.findings-naacl)
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DongJae Shin, HyeonSeok Lim, Inho Won, ChangSu Choi, Minjun Kim, SeungWoo Song, HanGyeol Yoo, SangMin Kim, KyungTae Lim
| Challenge: | constructing multilingual data for large multimodal models presents its own set of challenges due to language diversity and complexity. |
| Approach: | They propose to use GPT4-V to construct multimodal training datasets using a text-only version of GPT4. |
| Outcome: | The proposed method performs well in Korean and English, surpassing existing methods. |
Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models (2024.findings-emnlp)
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| Challenge: | HS is any communication demeaning a person or a group based on social or ethnic characteristics that undermines social harmony and individual safety . the recent Israel-Hamas conflict has escalated both anti-Muslim and anti-Semitic sentiments worldwide . |
| Approach: | They examine the role of large language models and large multimodal models in HS moderation . they examine how text, images, and audio interact to spread hate speech . |
| Outcome: | The findings highlight the need for solutions in low-resource settings and highlight the gaps in existing methods. |
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs (2025.coling-main)
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| Challenge: | Existing 3D AIGC methods don’t fully unleash human creativity. |
| Approach: | They propose a framework that generates 3D content from multimodal inputs . they propose 198 multimodal text inputs for 3D generation tasks . |
| Outcome: | The proposed framework generates 3D content from multimodal inputs without human intervention. |
MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding (2026.findings-eacl)
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Jeonghun Baek, Kazuki Egashira, Shota Onohara, Atsuyuki Miyai, Yuki Imajuku, Hikaru Ikuta, Kiyoharu Aizawa
| Challenge: | Manga is a richly multimodal narrative form that blends images and text in complex ways. |
| Approach: | They propose two benchmarks for multimodal manga understanding: mangaOCR and mangaVQA . mangaVQ consists of 526 high-quality, manually constructed question-answer pairs . |
| Outcome: | The proposed model is finetuned from the open-source LMM Qwen2.5-VL . it compares with proprietary models such as GPT-4o and Gemini 2.5 to evaluate its performance . |
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)
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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li
| Challenge: | Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion. |
| Approach: | They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree . |
| Outcome: | The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. |
M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025.emnlp-main)
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Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing
| Challenge: | Existing benchmarks for large multimodal models focus on short documents with less than 50 pages and are limited to extraction-based questions. |
| Approach: | They propose a retrieval-aware tuning approach to improve the accuracy of multimodal document reading by 4.6%. |
| Outcome: | The proposed framework improves the accuracy of model responses by 4.6% compared to existing benchmarks on documents with hundreds of pages and longer documents with more complex content. |
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)
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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang
| Challenge: | Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Approach: | They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Outcome: | The proposed benchmark is the first to scale task complexity while capturing diverse scenarios. |
UNICORN: A Unified Causal Video-Oriented Language-Modeling Framework for Temporal Video-Language Tasks (2024.emnlp-main)
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Yuanhao Xiong, Yixin Nie, Haotian Liu, Boxin Wang, Jun Chen, Rong Jin, Cho-Jui Hsieh, Lorenzo Torresani, Jie Lei
| Challenge: | Recent advances in large multimodal models have encouraged the development of large multi-modal models . however, it is unclear how to extend these models to the more complex video domain . |
| Approach: | They propose a visual instruction tuning framework to address temporal video-language tasks . they collect a dataset and fine-tune the framework on instruction-following data . |
| Outcome: | The proposed model can perform better on established temporal video-language tasks without training objectives and intensive pre-training. |
PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns (2024.findings-acl)
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| Challenge: | recognizing patterns and abstracting concepts are key to general intelligence, we show . state-of-the-art large multimodal models struggle to generalize well to simple abstract patterns . |
| Approach: | They evaluate large multimodal models with abstract patterns based on colors, numbers, sizes, and shapes. |
| Outcome: | The proposed model fails to generalize well to simple abstract patterns, the study shows . the model fails on single-concept puzzles, despite its sophistication . |
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data (2025.emnlp-main)
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| Challenge: | supervised fine-tuning (SFT) data is critical for large multimodal models . current methods suffer from factual errors and hallucinations due to inadequate visual perception . |
| Approach: | They propose a pipeline that integrates vision priors and state-of-the-art LMMs with statistical methods to enhance SFT data quality. |
| Outcome: | The proposed pipeline outperforms existing models in text-rich reasoning tasks while enhancing reasoning efficiency. |
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)
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| Challenge: | a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning. |
| Approach: | They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics. |
| Outcome: | The proposed model can be used to solve Olympiad-level physics problems. |
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs (2025.findings-acl)
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Omkar Thawakar, Dinura Dissanayake, Ketan Pravin More, Ritesh Thawkar, Ahmed Heakl, Noor Ahsan, Yuhao Li, Ilmuz Zaman Mohammed Zumri, Jean Lahoud, Rao Muhammad Anwer, Hisham Cholakkal, Ivan Laptev, Mubarak Shah, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Existing approaches do not emphasize step-wise problem-solving. |
| Approach: | They propose a visual reasoning chain benchmark and a fine-grained reasoning metric that evaluates correctness and logical coherence at each step. |
| Outcome: | The proposed framework outperforms existing models in six benchmarks and is 5x faster during inference scaling. |
Lightweight and Faithful Visual Condition Checking in Behavior Trees via Expert-Regularized Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing behavior trees are not suitable for high-dimensional perceptual inputs such as images or language. |
| Approach: | They propose a framework that leverages expert-regularized reinforcement learning to preserve semantic faithfulness while employing a factorized policy that aggregates sequential condition-node decisions into a single decision unit. |
| Outcome: | The proposed framework outperforms imitation learning and reinforcement learning but risks misalignment of condition nodes with intended semantics and poor credit assignment. |
Success and Cost Elicit Convention Formation for Efficient Communication (2026.acl-long)
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| Challenge: | ad hoc conventions allow people to coordinate on short, less costly utterances that are understood using shared conversational context. |
| Approach: | They propose a method to train large multimodal models to form conventions . they use simulated reference games to produce training data . |
| Outcome: | The proposed method reduces message length by up to 41% while increasing success by 15% over the course of the interaction. |