@inproceedings{wu-etal-2026-cova,
title = "{C}o{VA}-{SFT}: A Large-Scale Dataset for Chain of Visual Abstractions",
author = "Wu, Tsung-Han and
Lee, Heekyung and
Ji, Anya and
Chen, Haoming and
Darrell, Trevor and
Gonzalez, Joseph E. and
Chan, David M.",
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.526/",
pages = "11036--11051",
ISBN = "979-8-89176-462-0",
abstract = "Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effective for linguistic tasks, text-only CoT forces models to serialize visual problems into awkward prose. Although architectural solutions exist to process visual inputs, the community lacks a massive, multi-step, self-corrected dataset to teach models how to build and maintain internal visual workspaces when solving purely textual reasoning problems. To address this limitation, we introduce CoVA-SFT, a highly structured corpus of 51.9K samples containing over 222K multimodal reasoning steps across 5 distinct layout families and 17 complex tasks, and CoVA-Bench, a companion benchmark of 1,700 held-out test samples spanning the same tasks for reproducible evaluation. By providing explicit rationale formulations, agentic renderings, and verification loops, CoVA-SFT teaches multimodal language models to interleave text and visual abstractions. We validate the dataset by demonstrating that models fine-tuned on CoVA-SFT outperform all interleaved CoT baselines by more than 2{\texttimes} on average on CoVA-Bench, though they still fall short of strong text-only CoT baselines, highlighting open challenges for future work."
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<abstract>Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effective for linguistic tasks, text-only CoT forces models to serialize visual problems into awkward prose. Although architectural solutions exist to process visual inputs, the community lacks a massive, multi-step, self-corrected dataset to teach models how to build and maintain internal visual workspaces when solving purely textual reasoning problems. To address this limitation, we introduce CoVA-SFT, a highly structured corpus of 51.9K samples containing over 222K multimodal reasoning steps across 5 distinct layout families and 17 complex tasks, and CoVA-Bench, a companion benchmark of 1,700 held-out test samples spanning the same tasks for reproducible evaluation. By providing explicit rationale formulations, agentic renderings, and verification loops, CoVA-SFT teaches multimodal language models to interleave text and visual abstractions. We validate the dataset by demonstrating that models fine-tuned on CoVA-SFT outperform all interleaved CoT baselines by more than 2× on average on CoVA-Bench, though they still fall short of strong text-only CoT baselines, highlighting open challenges for future work.</abstract>
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%0 Conference Proceedings
%T CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions
%A Wu, Tsung-Han
%A Lee, Heekyung
%A Ji, Anya
%A Chen, Haoming
%A Darrell, Trevor
%A Gonzalez, Joseph E.
%A Chan, David M.
%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 wu-etal-2026-cova
%X Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps. While CoT is widely effective for linguistic tasks, text-only CoT forces models to serialize visual problems into awkward prose. Although architectural solutions exist to process visual inputs, the community lacks a massive, multi-step, self-corrected dataset to teach models how to build and maintain internal visual workspaces when solving purely textual reasoning problems. To address this limitation, we introduce CoVA-SFT, a highly structured corpus of 51.9K samples containing over 222K multimodal reasoning steps across 5 distinct layout families and 17 complex tasks, and CoVA-Bench, a companion benchmark of 1,700 held-out test samples spanning the same tasks for reproducible evaluation. By providing explicit rationale formulations, agentic renderings, and verification loops, CoVA-SFT teaches multimodal language models to interleave text and visual abstractions. We validate the dataset by demonstrating that models fine-tuned on CoVA-SFT outperform all interleaved CoT baselines by more than 2× on average on CoVA-Bench, though they still fall short of strong text-only CoT baselines, highlighting open challenges for future work.
%U https://aclanthology.org/2026.findings-emnlp.526/
%P 11036-11051
Markdown (Informal)
[CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions](https://aclanthology.org/2026.findings-emnlp.526/) (Wu et al., Findings 2026)
ACL
- Tsung-Han Wu, Heekyung Lee, Anya Ji, Haoming Chen, Trevor Darrell, Joseph E. Gonzalez, and David M. Chan. 2026. CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 11036–11051, Budapest, Hungary. Association for Computational Linguistics.