Papers by Mingyi Zhou

3 papers
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science (2025.findings-emnlp)

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Challenge: Large language models (LLMs) have advanced the automation of data science workflows, yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice.
Approach: They propose a benchmark to evaluate how large language models handle external domain knowledge in tabular prediction tasks.
Outcome: The proposed model evaluates whether it can critically leverage external domain knowledge as human data scientists do in practice.
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)

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Challenge: Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited.
Approach: They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering.
Outcome: The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering.
UICOMPASS: UI Map Guided Mobile Task Automation via Adaptive Action Generation (2025.emnlp-main)

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Challenge: Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android.
Approach: They propose a UI Map-guided LLM-based approach to automate mobile tasks using static analysis and LLMs.
Outcome: The proposed approach achieves a 15.87% higher task execution success rate than SOTA approaches even when only APK is available.

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