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Spark-to-Paper is an innovative end-to-end system for generating research papers that integrates thirteen composable skills within a coding assistant, effectively bridging the gap between literature retrieval, experiment execution, and manuscript preparation. By separating model-based judgment from deterministic operations and implementing a robust integrity check system, it ensures high citation validity and figure editability while maintaining the integrity of research claims throughout the process. The system demonstrates remarkable performance, achieving 99.5% citation validity and 96.4% figure editability across various research topics, indicating a significant advancement in automated research paper generation.
Achieving 99.5% citation validity and 96.4% figure editability, Spark-to-Paper transforms how research papers can be generated with unprecedented reliability and efficiency.
Turning a research idea into a complete paper requires more than text generation: the system must retrieve literature, design and execute experiments, revise claims according to evidence, produce publication-ready figures, and maintain consistency across a long generation process. We present Spark-to-Paper, an end-to-end research paper generation system implemented as thirteen composable skills inside an existing coding assistant, without requiring a separate agent platform or orchestration service. Spark-to-Paper separates model-based judgment from deterministic operations that can be directly executed and checked. It further separates experiment planning from reporting, so that required evidence is specified before results are observed and manuscript claims are revised according to measured outcomes. To improve reliability over long research trajectories, the system combines deterministic integrity checks with self-critique and bounds a failure mode we call the Self-Refutation Loop, in which repeated experiments continue to reject the original research objective. Spark-to-Paper also produces editable vector figures through programmatic plotting for experimental results and code-based reconstruction for generated method diagrams. Across eight controlled research topics, Spark-to-Paper achieves 99.5% citation validity and 96.4% figure editability. A controlled ablation increases fabrication detection from 14% for a single-pass draft to 92% with the full integrity and review stack, while adversarial review achieves 74% precision. The full system uses 11.9M tokens, costs $8.1 per manuscript, and requires 3.2 hours on average. These results show that end-to-end research paper generation can be implemented as a lightweight, composable workflow inside existing coding assistants while keeping experimental evidence central to how claims are accepted, revised, or abandoned.
