DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

arXiv:2601.13591 · cs.AI, cs.CL · Submitted 2026-01-20 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-01-20

Updated: 2026-09-07

Code: https://github.com/MiniMax-AI/MiniMax-M2

Project page: https://dsaeval.github.io/DSAEval

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Time Series — 12× in this paper
• Analyzer — 8× in this paper · explained in 1 episode
• Clustering — 8× in this paper · explained in 2 episodes
• Feature Engineering — 8× in this paper · explained in 3 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Natural Language Processing — 7× in this paper
• Core — 5× in this paper · explained in 2 episodes
• Large Language Model — 5× in this paper
• Trace — 5× in this paper · explained in 2 episodes
• Hallucination — 4× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Evaluation Dimensions — 2× in this paper · explained in 3 episodes
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Frustration — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
• Qwen3 Technical Report
• DeepAnalyze: Agentic Large Language Models for Autonomous Data Science

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