{"slug": "prompt-to-paper-agentic-ai-system-for-bioinformatics", "title": "Prompt-to-Paper: Agentic AI System for Bioinformatics", "summary": "Researchers introduced Prompt-to-Paper, a multi-agent AI system that generates verifiable bioinformatics manuscripts by grounding claims in 60-100 cited papers, executing real computational experiments, and scoring quality across eight dimensions. The system improved manuscript quality by an average of 17.96 points on a 0-100 scale and produced papers at $0.31 each, with human reviewers rating them 7.0 out of 10.", "body_md": "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. Second, an autonomous coding agent executes real computational biology experiments replacing synthetic outputs with genuine numerical results. Third, an eight-dimensional automated quality scorer, benchmarked with approximate reference statistics from published papers and augmented with explicit hallucination penalties, provides standardized, reproducible quality assessments. The quality-driven improvement loop uses a context-rich reviser that routes each iteration to one of three researcher actions and fires a deep research cycle every ten iterations to re-run experiments and re-manuscript from stronger outputs. We validate the system on five bioinformatics case studies; all five cases compiled submission-formatted PDFs with zero out-of-range citations. The improvement loop raises manuscript quality by an average of +17.96 points on a 0--100 scale (maximum +26.04. As partial external checks, a human reviewer scored the five manuscripts at an average of 7.0 out of 10. Complete manuscripts are produced at approximately 0.31 USD per paper.", "url": "https://wpnews.pro/news/prompt-to-paper-agentic-ai-system-for-bioinformatics", "canonical_source": "https://arxiv.org/abs/2607.05456", "published_at": "2026-07-08 04:00:00+00:00", "updated_at": "2026-07-08 04:04:14.094299+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents", "ai-research", "generative-ai"], "entities": ["Prompt-to-Paper", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/prompt-to-paper-agentic-ai-system-for-bioinformatics", "markdown": "https://wpnews.pro/news/prompt-to-paper-agentic-ai-system-for-bioinformatics.md", "text": "https://wpnews.pro/news/prompt-to-paper-agentic-ai-system-for-bioinformatics.txt", "jsonld": "https://wpnews.pro/news/prompt-to-paper-agentic-ai-system-for-bioinformatics.jsonld"}}