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Evidence record 11745 · automatically gathered

DSWorld: A Data Science World Model for Efficient Autonomous Agents

Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations before real execution. In this paper, we introduce the concept of Data Science World Model, which model the data science execution environment by predicting environment state transitions conditioned on current workflow sta

Record details

Published: 16 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 20 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Agents & autonomy

DSWorld: A Data Science World Model for Efficient Autonomous Agents

arXiv cs.AI · 17 July 2026

Shared topic: Agents & autonomy · Shared topic: Environment

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Shared topic: Agents & autonomy · Shared topic: Environment

Environment-free Synthetic Data Generation for API-Calling Agents

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How to cite this record

ethics.ai (16 July 2026), “DSWorld: A Data Science World Model for Efficient Autonomous Agents,” evidence record 11745, https://ethics.ai/record/11745 (originally published by HuggingFace Daily Papers).

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