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Story2Proposal is introduced, a multi-agent framework that generates structured scientific manuscripts by coordinating architect, writer, refiner, and renderer agents under a persistent shared visual contract. This contract tracks section structure and registered visual elements, ensuring alignment between narrative, evidence, and visuals. Experiments using the Jericho research corpus show Story2Proposal significantly outperforms DirectChat and improves upon the structured generation baseline Fars in expert evaluations.
Coordinating multiple agents with a shared visual contract can dramatically improve the structural consistency and visual alignment of automatically generated scientific papers.
Generating scientific manuscripts requires maintaining alignment between narrative reasoning, experimental evidence, and visual artifacts across the document lifecycle. Existing language-model generation pipelines rely on unconstrained text synthesis with validation applied only after generation, often producing structural drift, missing figures or tables, and cross-section inconsistencies. We introduce Story2Proposal, a contract-governed multi-agent framework that converts a research story into a structured manuscript through coordinated agents operating under a persistent shared visual contract. The system organizes architect, writer, refiner, and renderer agents around a contract state that tracks section structure and registered visual elements, while evaluation agents supply feedback in a generate evaluate adapt loop that updates the contract during generation. Experiments on tasks derived from the Jericho research corpus show that Story2Proposal achieved an expert evaluation score of 6.145 versus 3.963 for DirectChat (+2.182) across GPT, Claude, Gemini, and Qwen backbones. Compared with the structured generation baseline Fars, Story2Proposal obtained an average score of 5.705 versus 5.197, indicating improved structural consistency and visual alignment.
