@inproceedings{liu-etal-2026-dsflow,
title = "{DSF}low: Evolutionary Workflow Optimization for Generalizable {LLM}-Based Data Science Automation",
author = "Liu, Fan and
Pang, Tianyu and
Du, Chao and
Liu, Hao",
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.1027/",
pages = "21705--21720",
ISBN = "979-8-89176-462-0",
abstract = "Large language model (LLM) agents create new opportunities for end-to-end data science automation, but existing systems often overfit to dataset-specific schemas, preprocessing routines, and code patterns, making them brittle under task shifts and costly to reuse. We propose DSFlow, a workflow-optimization framework that represents data-science solutions as compositions of reusable operators rather than monolithic, dataset-coupled programs. DSFlow evolves operator-based workflows to discover transferable structures, expands its operator library through synthesis and composition, and uses a two-stage evaluation strategy that filters candidates with inexpensive plan-level scoring before selectively running full executions. This design keeps optimization practical while improving transfer to unseen tasks. Experiments on heterogeneous benchmarks spanning tabular, image, text, and time-series modalities show that DSFlow achieves strong zero-shot generalization, reaching an average rank of 2.62. In addition, DSFlow grows its operator library from 10 manually designed operators to more than 70 discovered ones, improving accuracy from 0.8070 to 0.8448 and demonstrating scalable knowledge accumulation."
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<abstract>Large language model (LLM) agents create new opportunities for end-to-end data science automation, but existing systems often overfit to dataset-specific schemas, preprocessing routines, and code patterns, making them brittle under task shifts and costly to reuse. We propose DSFlow, a workflow-optimization framework that represents data-science solutions as compositions of reusable operators rather than monolithic, dataset-coupled programs. DSFlow evolves operator-based workflows to discover transferable structures, expands its operator library through synthesis and composition, and uses a two-stage evaluation strategy that filters candidates with inexpensive plan-level scoring before selectively running full executions. This design keeps optimization practical while improving transfer to unseen tasks. Experiments on heterogeneous benchmarks spanning tabular, image, text, and time-series modalities show that DSFlow achieves strong zero-shot generalization, reaching an average rank of 2.62. In addition, DSFlow grows its operator library from 10 manually designed operators to more than 70 discovered ones, improving accuracy from 0.8070 to 0.8448 and demonstrating scalable knowledge accumulation.</abstract>
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%0 Conference Proceedings
%T DSFlow: Evolutionary Workflow Optimization for Generalizable LLM-Based Data Science Automation
%A Liu, Fan
%A Pang, Tianyu
%A Du, Chao
%A Liu, Hao
%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 liu-etal-2026-dsflow
%X Large language model (LLM) agents create new opportunities for end-to-end data science automation, but existing systems often overfit to dataset-specific schemas, preprocessing routines, and code patterns, making them brittle under task shifts and costly to reuse. We propose DSFlow, a workflow-optimization framework that represents data-science solutions as compositions of reusable operators rather than monolithic, dataset-coupled programs. DSFlow evolves operator-based workflows to discover transferable structures, expands its operator library through synthesis and composition, and uses a two-stage evaluation strategy that filters candidates with inexpensive plan-level scoring before selectively running full executions. This design keeps optimization practical while improving transfer to unseen tasks. Experiments on heterogeneous benchmarks spanning tabular, image, text, and time-series modalities show that DSFlow achieves strong zero-shot generalization, reaching an average rank of 2.62. In addition, DSFlow grows its operator library from 10 manually designed operators to more than 70 discovered ones, improving accuracy from 0.8070 to 0.8448 and demonstrating scalable knowledge accumulation.
%U https://aclanthology.org/2026.findings-emnlp.1027/
%P 21705-21720
Markdown (Informal)
[DSFlow: Evolutionary Workflow Optimization for Generalizable LLM-Based Data Science Automation](https://aclanthology.org/2026.findings-emnlp.1027/) (Liu et al., Findings 2026)
ACL