Agentic systems increasingly automate pieces of AI research. Yet turning underspecified research objectives into runnable, experimentally validated ML systems remains a central bottleneck. We study this operational setting as long-horizon ML research engineering: converting a research specification into a runnable ML system through repeated implementation, experimentation, and refinement. The central challenge is to sustain cumulative project progress across heterogeneous stages under delayed, confounded feedback. We introduce AiScientist, a multi-agent system built around thin control over thick state: a lightweight hierarchical research team coordinates through a File-as-Bus workspace that preserves decision-relevant artifacts across roles and invocations. On PaperBench, AiScientist improves over the strongest matched baselines by 9.92 and 11.15 points with Gemini-3-Flash and GLM-5, respectively. On MLE-Bench Lite, it reaches 81.82 Any Medal% under both backbones, improving over the strongest matched baselines by 4.55 and 16.67 points, and exceeding a Codex/GPT-5.5 xhigh frontier harness reference by 13.64 Any Medal points. Ablations and process analyses show that durable project state is central to later-round refinement: removing File-as-Bus lowers PaperBench score by 6.41 points and MLE-Bench Lite Any Medal% by 31.82 points. These results suggest that long-horizon AI research is not only a problem of stronger local reasoning, but a systems problem of maintaining cumulative, inspectable project progress.

Achieving mastery in real-world software engineering tasks is fundamentally bottlenecked by the scarcity of large-scale, high-quality training data. Scaling such data has been limited by the complexity of environment setup, unit-test generation, and problem statement curation. In this paper, we propose Scale-SWE, an automated, sandboxed multi-agent workflow designed to construct high-quality SWE data at scale. The system coordinates three specialized agents—for environment setup, test creation, and problem description synthesis—to process 6 million pull requests across 5.2k repositories, producing Scale-SWE-Data: 100k verified SWE instances, the largest such dataset to date. It substantially surpasses existing real-world datasets in repository diversity and reflects realistic task complexity. We further demonstrate the dataset’s utility for training by distilling 71,498 high-quality trajectories and fine-tuning Qwen-30B-A3B-Instruct to produce Scale-SWE-Agent. Our agent achieves a 64% resolve rate on SWE-Bench-Verified—a nearly three-fold improvement over the base model. Scale-SWE provides a scalable, reproducible approach for data construction to advance LLM-based software engineering.