Story2Proposal: A Scaffold for Structured Scientific Paper Writing
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.
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Zhuoyang Qian
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Wei Shi
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Xu Lin
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Li Ling
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Meng Luo
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Ziming Wang
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Zhiwei Zhang
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Tengyue Xu
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Gaoge Liu
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Zhentao Zhang
admin_assigned
2026-03-31T09:48:32.400Z
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zhentao zhang
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Shuo Zhang
claimed_verified
2026-04-02T08:17:23.653Z
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Zhentao Zhang
santasoda
user
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Ziqi Wang
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Zheng Feng
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Yan Luo
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Shu Xu
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Yongjin Chen
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Zhibo Feng
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Zhuo Chen
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Bruce Yuan
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Biao Wu
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Harry Wang
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Kris Chen
Story2Proposal: A Scaffold for Structured Scientific Paper Writing
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.
Story2Proposal is a contract-governed multi-agent framework that generates structured scientific manuscripts with improved consistency and visual alignment through coordinated agents operating under a shared visual contract.
multi-agent framework visual contract structured manuscript generation generate evaluate adapt loop research story scientific manuscripts