Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture

arXiv 2026 37.5 method

TLDR

An architecture using deterministic integrity gates to verify LLM-generated clinical manuscripts, evaluated on public datasets and defect ablation.

Reasoning

The paper presents a novel verification architecture with deterministic checks, which is a strength. However, it is narrowly focused on clinical manuscript preparation and does not address broader automated discovery or experimentation. The evaluation is thorough but limited to specific pipelines.

Read-first score

Read-first score 37.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 22.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=code,dataset

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=ablation,dataset

Topical relevance 42%

18.3

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 31.

Keyword Scores

AI for scientific research

paper writing agent

autonomous research agent

research automation

AI scientist

automated research

automated scientific discovery

literature review agent

scientific discovery agent

survey generation

automated experimentation

experiment design agent

Resources

Paper