Large language model (LLM) agents create new opportunities for end-to-end data science automation, but existing systems often overfit to dataset-specific schemas, preprocessing routines, and code patterns, making them brittle under task shifts and costly to reuse. We propose DSFlow, a workflow-optimization framework that represents data-science solutions as compositions of reusable operators rather than monolithic, dataset-coupled programs. DSFlow evolves operator-based workflows to discover transferable structures, expands its operator library through synthesis and composition, and uses a two-stage evaluation strategy that filters candidates with inexpensive plan-level scoring before selectively running full executions. This design keeps optimization practical while improving transfer to unseen tasks. Experiments on heterogeneous benchmarks spanning tabular, image, text, and time-series modalities show that DSFlow achieves strong zero-shot generalization, reaching an average rank of 2.62. In addition, DSFlow grows its operator library from 10 manually designed operators to more than 70 discovered ones, improving accuracy from 0.8070 to 0.8448 and demonstrating scalable knowledge accumulation.

Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality. While existing benchmarks focus on basic plan validity, they neglect critical aspects such as route efficiency, POI appeal, and real-time adaptability. This paper introduces TP-RAG, the first benchmark tailored for retrieval-augmented, spatiotemporal-aware travel planning. Our dataset includes 2,348 real-world travel queries, 85,575 fine-grain annotated POIs, and 18,784 high-quality travel trajectory references sourced from online tourist documents, enabling dynamic and context-aware planning. Through extensive experiments, we reveal that integrating reference trajectories significantly improves spatial efficiency and POI rationality of the travel plan, while challenges persist in universality and robustness due to conflicting references and noisy data. To address these issues, we propose EvoRAG, an evolutionary framework that potently synergizes diverse retrieved trajectories with LLMs’ intrinsic reasoning. EvoRAG achieves state-of-the-art performance, improving spatiotemporal compliance and reducing commonsense violation compared to ground-up and retrieval-augmented baselines. Our work underscores the potential of hybridizing Web knowledge with LLM-driven optimization, paving the way for more reliable and adaptive travel planning agents.