@inproceedings{zhu-etal-2026-omnida,
title = "{O}mni{DA}: Grounding Agentic Multimodal Data Analysis with Evidence-Aware Process Rewards",
author = "Zhu, Xuhang and
Li, Liyao and
Wang, Ningtao and
Chen, Yanyu and
Chen, Hao and
Ye, Wentao and
Wang, Ziquan and
Zhai, Haoxing and
Cheng, Yu and
Chen, Gang and
Tang, Xiu and
Wang, Haobo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1066/",
doi = "10.18653/2026.findings-emnlp.1066",
pages = "22552--22565",
ISBN = "979-8-89176-462-0",
abstract = "Real-world data analysis often requires joint reasoning over heterogeneous artifacts such as tables, charts, and textual reports. Training reliable multimodal large language model (MLLM) agents for this setting remains challenging, as large-scale aligned training data are scarce, and outcome-only rewards are too coarse to distinguish grounded cross-source analysis from spurious success. We introduce OmniDA, a training recipe that addresses these two challenges with a scalable multimodal data-synthesis pipeline and an Evidence-Aware Process Reward (EAPR) that supervises both evidence recovery and procedural validity. Through this pipeline, we construct OmniDA-11K, a high-quality training corpus for multimodal data analysis. When instantiated with Qwen3-VL-8B, OmniDA improves over the corresponding agentic baseline by 19.2 points on average across five challenging benchmarks and achieves the strongest overall performance among existing open-source data-analysis agents. These results highlight the value of supervising evidence recovery and analytical procedure beyond final-answer correctness."
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<abstract>Real-world data analysis often requires joint reasoning over heterogeneous artifacts such as tables, charts, and textual reports. Training reliable multimodal large language model (MLLM) agents for this setting remains challenging, as large-scale aligned training data are scarce, and outcome-only rewards are too coarse to distinguish grounded cross-source analysis from spurious success. We introduce OmniDA, a training recipe that addresses these two challenges with a scalable multimodal data-synthesis pipeline and an Evidence-Aware Process Reward (EAPR) that supervises both evidence recovery and procedural validity. Through this pipeline, we construct OmniDA-11K, a high-quality training corpus for multimodal data analysis. When instantiated with Qwen3-VL-8B, OmniDA improves over the corresponding agentic baseline by 19.2 points on average across five challenging benchmarks and achieves the strongest overall performance among existing open-source data-analysis agents. These results highlight the value of supervising evidence recovery and analytical procedure beyond final-answer correctness.</abstract>
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%0 Conference Proceedings
%T OmniDA: Grounding Agentic Multimodal Data Analysis with Evidence-Aware Process Rewards
%A Zhu, Xuhang
%A Li, Liyao
%A Wang, Ningtao
%A Chen, Yanyu
%A Chen, Hao
%A Ye, Wentao
%A Wang, Ziquan
%A Zhai, Haoxing
%A Cheng, Yu
%A Chen, Gang
%A Tang, Xiu
%A Wang, Haobo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhu-etal-2026-omnida
%X Real-world data analysis often requires joint reasoning over heterogeneous artifacts such as tables, charts, and textual reports. Training reliable multimodal large language model (MLLM) agents for this setting remains challenging, as large-scale aligned training data are scarce, and outcome-only rewards are too coarse to distinguish grounded cross-source analysis from spurious success. We introduce OmniDA, a training recipe that addresses these two challenges with a scalable multimodal data-synthesis pipeline and an Evidence-Aware Process Reward (EAPR) that supervises both evidence recovery and procedural validity. Through this pipeline, we construct OmniDA-11K, a high-quality training corpus for multimodal data analysis. When instantiated with Qwen3-VL-8B, OmniDA improves over the corresponding agentic baseline by 19.2 points on average across five challenging benchmarks and achieves the strongest overall performance among existing open-source data-analysis agents. These results highlight the value of supervising evidence recovery and analytical procedure beyond final-answer correctness.
%R 10.18653/2026.findings-emnlp.1066
%U https://aclanthology.org/2026.findings-emnlp.1066/
%U https://doi.org/10.18653/2026.findings-emnlp.1066
%P 22552-22565
Markdown (Informal)
[OmniDA: Grounding Agentic Multimodal Data Analysis with Evidence-Aware Process Rewards](https://aclanthology.org/2026.findings-emnlp.1066/) (Zhu et al., Findings 2026)
ACL
- Xuhang Zhu, Liyao Li, Ningtao Wang, Yanyu Chen, Hao Chen, Wentao Ye, Ziquan Wang, Haoxing Zhai, Yu Cheng, Gang Chen, Xiu Tang, and Haobo Wang. 2026. OmniDA: Grounding Agentic Multimodal Data Analysis with Evidence-Aware Process Rewards. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22552–22565, Budapest, Hungary. Association for Computational Linguistics.