Prompt-to-Paper: Agentic AI System for Bioinformatics

• URL: http://arxiv.org/abs/2607.05456v1
• Date: Sun, 05 Jul 2026 21:30:24 GMT
• Title: Prompt-to-Paper: Agentic AI System for Bioinformatics
• Abstract summary: We present Prompt-to-Paper, a multi-agent framework that directly addresses this evaluation gap through three integrated innovations.<n>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.<n>Second, an autonomous coding agent executes real computational biology experiments replacing synthetic outputs with genuine numerical results.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress [130.58520199343707]
  PaperDoctor is an agent framework for pre-submission feedback with three key innovations.<n>First, a holistic hierarchical framework evaluates writing, layout, references, code, theory, prior work, and experiments.<n>Second, each finding contains an observation, a pointer to specific evidence such as a sentence, equation, or code line, and a revision suggestion.<n>Third, PaperDoctor selectively rebuilds and reruns experiments based on claim importance and compute budget.
  arXiv  Detail & Related papers  (2026-09-15T11:08:11Z)
• Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation [60.151050016855834]
  DAS is a stateful agentic framework for generating publication-oriented academic surveys.<n>Its key idea is to separate reusable paper analysis from topic-specific manuscript construction.<n>DAS achieves the highest average in all four dimensions, with an overall score of 4.34 compared with 4.03 for the strongest competitor.
  arXiv  Detail & Related papers  (2026-08-18T17:29:00Z)
• ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence [57.37494162084001]
  Chain-of-Evidence (CoE) is a verifiability framework requiring every claim to be traceable to its evidence source.<n>CoE Audit is a post-hoc audit whose four integrity checks -- score verification, specification violation, reference verification, and method-code alignment -- apply uniformly to all systems.
  arXiv  Detail & Related papers  (2026-05-25T21:30:27Z)
• AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
  We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
  arXiv  Detail & Related papers  (2026-05-18T17:08:26Z)
• MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility [2.2969445973129123]
  We introduce MLReplicate, an end-to-end benchmark evaluating autonomous research systems on machine learning.<n>The benchmark was constructed from ICML 2025 papers reformulated into standardized input specifications and evaluated across 6 state-of-the-art research systems.<n>In contrast to automated reviews, human reviewers consistently identified methodological flaws, hallucinated experimental results, and failures across all systems.
  arXiv  Detail & Related papers  (2026-05-15T20:35:32Z)
• The Last Human-Written Paper: Agent-Native Research Artifacts [106.47848184955576]
  We introduce the Agent-Native Research Artifact (ARA), a protocol that replaces the narrative paper with a machine-executable research package.<n>Three mechanisms support the ecosystem: a Live Research Manager that captures decisions and dead ends during ordinary development; an ARA Compiler that translates legacy PDFs and repos into ARAs; and an ARA-native review system that automates objective checks so human reviewers can focus on significance, novelty, and taste.
  arXiv  Detail & Related papers  (2026-04-27T16:23:09Z)
• PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing [43.290959385976414]
  PaperOrchestra is a multi-agent framework for automated AI research paper writing.<n>It transforms unconstrained preconstrained materials into submission-ready manuscripts.<n>PaperWritingBench is the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers.
  arXiv  Detail & Related papers  (2026-04-06T18:00:00Z)
• NoveltyAgent: Autonomous Novelty Reporting Agent with Point-wise Novelty Analysis and Self-Validation [34.18769961207746]
  We introduce NoveltyAgent, a multi-agent system designed to generate comprehensive and faithful novelty reports.<n>It decomposes manuscripts into discrete novelty points for fine-grained retrieval and comparison, and builds a comprehensive related-paper database.<n>Experiments show that NoveltyAgent achieves state-of-the-art performance, outperforming GPT-5 DeepResearch by 10.15%.
  arXiv  Detail & Related papers  (2026-03-21T17:19:11Z)
• AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research [81.04845910798387]
  Generating natural language explanations for threat detections remains an open problem in cybersecurity research.<n>We present AutoMalDesc, an automated static analysis summarization framework that operates independently at scale.<n>We publish our complete dataset of more than 100K script samples, including annotated seed (0.9K) datasets, along with our methodology and evaluation framework.
  arXiv  Detail & Related papers  (2025-11-17T13:05:25Z)
• A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature [0.0]
  Autoregressive generative models have produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical.<n>We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories.<n>Experiments on corpora of up to 1000 papers demonstrate near-linear scalability with eight CPU workers.
  arXiv  Detail & Related papers  (2025-08-06T16:33:20Z)
• News Summarization and Evaluation in the Era of GPT-3 [73.48220043216087]
  We study how GPT-3 compares against fine-tuned models trained on large summarization datasets. We show that not only do humans overwhelmingly prefer GPT-3 summaries, prompted using only a task description, but these also do not suffer from common dataset-specific issues such as poor factuality.
  arXiv  Detail & Related papers  (2022-09-26T01:04:52Z)
• SummEval: Re-evaluating Summarization Evaluation [169.622515287256]
  We re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion. We benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics. We assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset.
  arXiv  Detail & Related papers  (2020-07-24T16:25:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.