@inproceedings{lyu-etal-2026-datasight,
title = "{D}ata{S}ight: Learning to Predict Before Executing for Data Analysis Agents",
author = "Lyu, Shangke and
Zheng, Zhe and
Wu, Linjuan and
Yu, Yan and
Zhang, Xuan and
Ma, Jin and
Shen, Yongliang and
Lu, Weiming",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.922/",
pages = "21229--21251",
ISBN = "979-8-89176-459-0",
abstract = "Data-analytic agents must iteratively generate and execute code while interpreting real-time feedback to refine their analytical strategies. Existing training paradigms decouple the agent{'}s policy from the underlying environment dynamics, producing agents that learn to recover from errors rather than to avoid them: failure rates rise alongside recovery rates, and interaction turns inflate as agents resort to trial-and-error exploration. We introduce DataSight, a framework that internalizes environment dynamics by making the model{'}s anticipation of execution outcomes an explicit, structured output accompanying every generated action. DataSight first enriches expert trajectories with foresight prediction fields grounded in real execution feedback, then applies a dual-stage RL curriculum that calibrates prediction fidelity before optimizing long-horizon task performance, and at inference time intercepts anticipated failures before physical execution. Trained on only 7,273 trajectories, DataSight-4B surpasses both DataMind-7B and DeepAnalyse-8B in average accuracy, while DataSight-8B achieves 64.13{\%} Avg@3 across four benchmarks with Pass@3 of 74.81{\%}, approaching DeepSeek-V3.2 at 75.56{\%}. Analysis confirms that foresight training generalizes across non-reasoning backbones and SQL-based tasks, improving both single-step code quality and multi-turn interaction efficiency."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lyu-etal-2026-datasight">
<titleInfo>
<title>DataSight: Learning to Predict Before Executing for Data Analysis Agents</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shangke</namePart>
<namePart type="family">Lyu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhe</namePart>
<namePart type="family">Zheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Linjuan</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yan</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xuan</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jin</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yongliang</namePart>
<namePart type="family">Shen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weiming</namePart>
<namePart type="family">Lu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Data-analytic agents must iteratively generate and execute code while interpreting real-time feedback to refine their analytical strategies. Existing training paradigms decouple the agent’s policy from the underlying environment dynamics, producing agents that learn to recover from errors rather than to avoid them: failure rates rise alongside recovery rates, and interaction turns inflate as agents resort to trial-and-error exploration. We introduce DataSight, a framework that internalizes environment dynamics by making the model’s anticipation of execution outcomes an explicit, structured output accompanying every generated action. DataSight first enriches expert trajectories with foresight prediction fields grounded in real execution feedback, then applies a dual-stage RL curriculum that calibrates prediction fidelity before optimizing long-horizon task performance, and at inference time intercepts anticipated failures before physical execution. Trained on only 7,273 trajectories, DataSight-4B surpasses both DataMind-7B and DeepAnalyse-8B in average accuracy, while DataSight-8B achieves 64.13% Avg@3 across four benchmarks with Pass@3 of 74.81%, approaching DeepSeek-V3.2 at 75.56%. Analysis confirms that foresight training generalizes across non-reasoning backbones and SQL-based tasks, improving both single-step code quality and multi-turn interaction efficiency.</abstract>
<identifier type="citekey">lyu-etal-2026-datasight</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.922/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>21229</start>
<end>21251</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T DataSight: Learning to Predict Before Executing for Data Analysis Agents
%A Lyu, Shangke
%A Zheng, Zhe
%A Wu, Linjuan
%A Yu, Yan
%A Zhang, Xuan
%A Ma, Jin
%A Shen, Yongliang
%A Lu, Weiming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lyu-etal-2026-datasight
%X Data-analytic agents must iteratively generate and execute code while interpreting real-time feedback to refine their analytical strategies. Existing training paradigms decouple the agent’s policy from the underlying environment dynamics, producing agents that learn to recover from errors rather than to avoid them: failure rates rise alongside recovery rates, and interaction turns inflate as agents resort to trial-and-error exploration. We introduce DataSight, a framework that internalizes environment dynamics by making the model’s anticipation of execution outcomes an explicit, structured output accompanying every generated action. DataSight first enriches expert trajectories with foresight prediction fields grounded in real execution feedback, then applies a dual-stage RL curriculum that calibrates prediction fidelity before optimizing long-horizon task performance, and at inference time intercepts anticipated failures before physical execution. Trained on only 7,273 trajectories, DataSight-4B surpasses both DataMind-7B and DeepAnalyse-8B in average accuracy, while DataSight-8B achieves 64.13% Avg@3 across four benchmarks with Pass@3 of 74.81%, approaching DeepSeek-V3.2 at 75.56%. Analysis confirms that foresight training generalizes across non-reasoning backbones and SQL-based tasks, improving both single-step code quality and multi-turn interaction efficiency.
%U https://aclanthology.org/2026.emnlp-main.922/
%P 21229-21251
Markdown (Informal)
[DataSight: Learning to Predict Before Executing for Data Analysis Agents](https://aclanthology.org/2026.emnlp-main.922/) (Lyu et al., EMNLP 2026)
ACL
- Shangke Lyu, Zhe Zheng, Linjuan Wu, Yan Yu, Xuan Zhang, Jin Ma, Yongliang Shen, and Weiming Lu. 2026. DataSight: Learning to Predict Before Executing for Data Analysis Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 21229–21251, Budapest, Hungary. Association for Computational Linguistics.