🤖 machine learning

CheckSupport: A Local LLM-Powered Tool for Automated Manuscript Submission Checklist Selection and Completion

CheckSupport is an open-source, locally deployable tool that leverages instruction-tuned large language models to automate the selection and evidence-grounded completion of scientific reporting checklists, achieving high accuracy and privacy preservation while significantly reducing the manual burden of manuscript submission.

Original authors: Satvik Tripathi, Don Enwerem, Kevin Song, Kristian Quevada, Jacinta Arnold, Tessa S. Cook

Published 2026-05-19

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CC BY 4.0

Original authors: Satvik Tripathi, Don Enwerem, Kevin Song, Kristian Quevada, Jacinta Arnold, Tessa S. Cook

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are a scientist who just finished a major research project. You have your data, your methods, and your results. Now, you need to write a paper to share it with the world. But before you can submit it, there's a catch: you must fill out a long, boring, and very specific "reporting checklist."

Think of these checklists (like CONSORT or PRISMA) as strict security forms for a high-security building. If you miss even one tiny box, the guards (journal editors) might turn you away. The problem is, there are dozens of different forms for different types of buildings, and figuring out which one you need—and then filling out every single line correctly—takes hours of manual work.

Enter CheckSupport.

The paper introduces CheckSupport, a free, open-source tool that acts like a super-smart, privacy-focused assistant sitting right on your own computer. It doesn't need to send your secret research data to the cloud; it keeps everything local, like a librarian who never leaves the library.

Here is how it works, broken down into simple steps:

1. The "Which Form Do I Need?" Detective

First, you hand your manuscript (your research paper) to CheckSupport. The tool reads the first few pages and acts like a detective. It asks itself: "Is this a study about a new drug? A diagnostic test? A machine learning model?"
Based on the clues in your text, it instantly picks the correct "security form" (the reporting checklist) from a list of options.

The Analogy: Imagine walking into a post office with a package. Instead of guessing which counter to go to, a robot scans your package and immediately hands you the exact form you need, saying, "Go to Counter 3, this is a Priority Mail form."

2. The "Fill-in-the-Blanks" Machine

Once it knows which form you need, CheckSupport starts filling it out. But here is the magic: it doesn't make things up.
It acts like a meticulous copy-paste artist. It scans your entire paper, finds the specific sentences where you described your methods, and copies that information into the checklist boxes.

The Analogy: Think of it like a highlighter pen with a brain. If the checklist asks, "Did you use a control group?", the tool scans your paper, finds the sentence "We used a control group of 50 patients," and writes that down. If your paper doesn't mention a control group, the tool doesn't guess or invent one. It simply writes "Not mentioned" or leaves it blank, ensuring honesty.

3. The "Local" Safety Vault

Many modern tools send your data to big servers in the cloud to be processed. CheckSupport is different. It runs entirely on your own computer using "local" AI.

The Analogy: Most tools are like sending your diary to a stranger to read and summarize. CheckSupport is like hiring a private secretary who works in your home office. They read your diary, fill out your forms, and then hand them back to you. Your diary never leaves the room. This is crucial for scientists who need to keep their unpublished data private.

4. How Good Is It?

The authors tested this tool on 100 real scientific papers about AI in radiology.

The Result: It picked the right checklist 90% of the time. When filling out the checklist, it got the details right 88% of the time.
The Speed: It did all of this in about 12.5 seconds per paper, even on a standard computer without fancy graphics cards.

The Big Picture

The paper argues that CheckSupport isn't trying to write the story for you. Instead, it's trying to organize the evidence you already wrote. It treats the AI not as a creative writer, but as a structured data extractor.

By automating the boring, repetitive part of the process (finding the right form and copying the right facts), it hopes to make scientific reporting faster and more honest, without compromising privacy or accuracy. It's a tool to help scientists get their paperwork done so they can focus on the science.

Technical Summary: CheckSupport

Problem Statement
Transparent and standardized reporting is critical for the reproducibility of scientific research, yet adherence to reporting guidelines (e.g., CONSORT, PRISMA, STARD) remains inconsistent. The primary barriers are the substantial manual effort and domain expertise required to identify the appropriate checklist for a specific study and to accurately complete it. Existing automated solutions often rely on cloud-based Large Language Models (LLMs), raising significant concerns regarding data privacy, cost, reproducibility, and long-term accessibility. Furthermore, current applications of LLMs in science often focus on generative text synthesis, which may not align with the need for faithful, evidence-grounded extraction in scientific reporting.

Methodology
The authors present CheckSupport, an open-source, locally deployable command-line tool designed to automate checklist selection and completion. The system operates entirely offline using the Ollama framework, ensuring that manuscript data never leaves the user's local hardware.

Architecture and Workflow: The system follows a modular pipeline: manuscript ingestion (PDF/DOCX/Text) $\rightarrow$ checklist identification $\rightarrow$ checklist parsing $\rightarrow$ content extraction $\rightarrow$ report generation. It supports both standardized guidelines and custom user-defined checklists.
Staged Prompting Strategy: Rather than using LLMs as free-form generators, CheckSupport treats them as constrained inference components. The process is decomposed into four sequential stages:
1. Checklist Recommendation: A one-shot, single-label classification task where the model selects one checklist from a fixed list based on a truncated manuscript excerpt (first 2,000 characters).
2. Preprocessing and Guidance Generation: The model infers general and section-specific guidance on what information to seek, using a higher temperature (0.7) to elicit descriptive advice.
3. Section-Level Text Extraction: The system extracts relevant manuscript portions for each checklist section using fixed 5,000-character windows.
4. Item-Level Answer Generation: For each checklist item, the model generates a response based on the item text, parsed instructions, and the previously extracted section content.
Inference Controls: To prioritize accuracy and reproducibility, the system employs low temperatures for classification (0.2) and extraction (0.3–0.5), uses fixed context windows to avoid dependence on extended-context models, and enforces strict output constraints. It explicitly permits and encourages "null" responses when information is absent to prevent hallucination.
Model Configuration: The system is model-agnostic but defaults to instruction-tuned models like Llama 3.1, capable of running on consumer-grade CPU-only hardware.

Key Contributions

Local Execution Framework: CheckSupport provides a fully local, privacy-preserving alternative to cloud-based AI tools, addressing data security concerns in sensitive research environments.
Structured Inference Approach: The paper introduces a staged prompting methodology that decomposes complex reporting tasks into constrained inference steps, prioritizing faithful extraction over generative fluency.
Open-Source Implementation: The tool is released as open-source software, allowing for transparency, auditing, and adaptation to various reporting standards.
Dual-Functionality: It automates both the selection of the correct reporting guideline and the evidence-grounded completion of that guideline.

Results
Evaluated on a corpus of 100 peer-reviewed manuscripts (indexed under "AI in radiology" from 2025), CheckSupport demonstrated the following performance on CPU-only hardware:

Checklist Recommendation: Achieved 90% overall accuracy. Performance varied by category: 86.7% for complex "Type A" studies (new LLM structures/fine-tuning), 100% for "Type B" studies (practical applications), and 80% for manuscripts where no checklist was applicable.
Checklist Completion: Achieved 88% overall item-level accuracy. Accuracy was higher for "Type B" manuscripts (93.3%) compared to "Type A" (80.0%).
Runtime: The average wall-clock time per manuscript was 12.5 seconds, including both recommendation and full completion.
Error Analysis: Errors primarily stemmed from ambiguous manuscript language, incomplete reporting in the source text, or information falling outside the bounded context windows. Notably, the system produced no instances of unsupported or fabricated content, successfully flagging missing information as null responses.

Significance and Claims
The authors position CheckSupport not as a tool for generating scientific content, but as infrastructure for research governance. The paper claims that by shifting the burden of checklist compliance from manual effort to structured, assistive automation, the system supports more transparent and reproducible scientific reporting.

Key claims regarding significance include:

Reframing LLM Utility: The work demonstrates that LLMs can be effectively utilized for methodological support and rigorous extraction rather than narrative generation.
Privacy and Trust: By operating locally, the system addresses growing concerns about data leakage and proprietary dependencies, fostering institutional trust.
Human-in-the-Loop: The authors emphasize that the system is designed to augment, not replace, human oversight. It makes reporting gaps explicit rather than implicitly filling them, allowing researchers and reviewers to focus on areas requiring expert judgment.
Cultural Shift: The tool aims to lower the barrier to checklist completion, potentially reducing reviewer fatigue and encouraging the view of reporting guidelines as integral components of the research process rather than administrative obligations.