DSWorld: A Data Science World Model for Efficient Autonomous Agents
- Published
- Source
- arXiv
- Paper number
- 663
- Field
- AI / General
- arXiv ID
- 2607.15901
Key points
- It is the first to propose the concept of a data science world model that predicts state transitions in data science tasks.
- It introduces cost-aware routing, where lightweight tasks are executed directly and heavier tasks are predicted by an LLM simulator.
- It speeds up RL agent training by about 14 times and retrieval-based reasoning by 3 to 6 times.
- On the transition-prediction task, it achieves accuracy that is 35.6 percent higher than the strongest LLM baseline.
Paper links
External research summaries. These are not HDATF publications or measured product results.