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Second, an autonomous coding agent executes real computational biology experiments replacing synthetic outputs with genuine numeri","title":"Prompt-to-Paper: Agentic AI System for Bioinformatics","url":"https://arxiv.org/abs/2607.05456","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.05456v1 Announce Type: new \nAbstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication. We present Prompt-to-Paper, a multi-agent framework that directly addresses this evaluation gap through three integrated innovations. First, a deterministic retrieval-augmented generation pipeline with section-aware relevance scoring and snowball citation expansion grounds every claim in a verifiable corpus of 60--100 papers. 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