Prompt-to-Paper: Agentic AI System for Bioinformatics
Quick Answer
Prompt-to-Paper introduces a multi-agent AI framework that enhances bioinformatics manuscript generation by grounding claims in verifiable literature, executing real experiments, and providing standardized quality assessments, achieving an average quality increase of 17.96 points on a 0-100 scale at a cost of approximately $0.31 per paper.
Quick Take
Prompt-to-Paper introduces a AI framework that enhances bioinformatics manuscript generation by grounding claims in verifiable literature, executing real experiments, and providing standardized quality assessments, achieving an average quality increase of 17.96 points on a 0-100 scale at a cost of approximately $0.31 per paper.
Key Points
- Integrates a pipeline for claim verification.
- Autonomous coding agent conducts real computational biology experiments.
- Automated quality scorer benchmarks against published papers with hallucination penalties.
- Achieves an average manuscript quality increase of 17.96 points.
- Produces complete manuscripts at a cost of approximately $0.31 each.
Paper Resources
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~2 min readAbstract: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.
| Comments: | NA |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2607.05456 [cs.AI] |
| (or arXiv:2607.05456v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05456 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muhammad Usman Shahid Khan Khan [view email]
[v1]
Sun, 5 Jul 2026 21:30:24 UTC (68,454 KB)
— Originally published at arxiv.org
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