White Paper Universal Template

Uploaded: 10th of August, 2026

Author: Aleksandr Slokov

This is a general mindset framework for all empirical scientific papers. Instead of starting right away with long paragraphs, use this document as a list of interview questions. If you can answer all these questions, you already have 80% of the paper’s content.

Title & Info

Title: What does this research do, what problem does it solve, or what outstanding method does it use? (Keep it concise, include key words for easy searching).
Authors: Who contributed? Affiliations? Who is the Corresponding author?

Abstract

The most important paragraph (150 - 250 words). Many people only read this part to decide whether to read further. Write this part LAST.

[Context]: What is the broader problem you are solving? Why is it important?
[The Gap]: What are the fatal limitations/shortcomings of current solutions?
[Solution]: What new method/approach do you propose to fill that gap?
[Results]: What is the most prominent quantitative (numbers) or qualitative evidence proving your solution is effective?
[Implications]: What is the greatest contribution of this research to practice or academia?

1. Introduction

This section is an information funnel: Going from the big picture narrowing down to your main point.

Opening: What is this field and why should society/academia care about it?
State-of-the-art: What has been done already?
The Problem/Gap: Despite much work, what is the core barrier that no one has completely solved yet? (Example: Slow, expensive, inaccurate, not applicable to environment X…).
The Proposed Approach: In this paper, how do we solve the above problem?
Key Contributions: Bullet point 3-4 of the biggest contributions of the paper. (E.g., “First, we propose architecture X… Second, we publish dataset Y… Third, we demonstrate a Z% improvement…”).

This is not listing history, but building an argument: “People have done X, Y, Z, but it is still not enough”.

Method Group 1: State the most related studies, their advantages.
Method Group 2 (if any): State other trending approaches.
The Distinction: Affirm the DIFFERENCE. How does my system/method overcome the weaknesses of the above groups? (Where does your research stand in this picture?).

3. Methodology / Proposed Method

This is the heart of the paper. Answer the question: What did you DO and HOW did you do it?

Important note: Always start with a very clear Flowchart/Architecture Diagram.
3.1. Method Overview: Looking at the diagram, what does the big picture look like running from A to Z?
3.2. Core Components (Innovation Focus): Delve into the newest thing you created.
- What is the mathematical/theoretical basis? (Write formulas if any).
- How does the algorithm work?
3.3. Auxiliary Steps/Components: Other components for the system to run smoothly. Detailed enough so others can reproduce your model.

4. Theoretical Analysis / Justification (Optional but highly recommended)

Persuade the reader with scientific reasoning that the choice in Section 3 is correct.

Why choose this design and not another design?
Compare the complexity (time, space), cost, or feasibility of the new method with the old method theoretically (no need to run experiments yet).

5. Experimental Setup

Maximum transparency so the paper has scientific credibility.

Datasets / Materials: Where from? What size? What characteristics? How was it pre-processed?
Environment / Hardware: What machine did it run on, what software/chemicals/measuring equipment was used?
Evaluation Metrics: How to know if it is “good”? (Use accuracy, error rate, run time, or what scale?).
Baseline: Which old methods will you pit your method against to prove superiority?

6. Results & Discussion

Let the numbers and charts speak.

6.1. Main Results:
- Provide a Table comparing your results with the Baselines.
- Comment on the table: In which criteria is your method superior? Specific numbers (X times faster, Y% more accurate).
6.2. Ablation Studies / Sensitivity Analysis:
- Question: Are you sure the new component you added (in section 3) actually has an effect?
- Method: Turn off/Remove each part of the system and see if the results get worse. This proves that no part of your design is redundant.
6.3. Limitations & Failure Cases:
- Be honest: Are there cases where your method works incorrectly/poorly? Why? (This honesty increases your scientific credibility).

7. Conclusion

Leave a lasting impression on the reader.

Summary: Reiterate in 1-2 sentences the biggest solution and achievement (like the Abstract but using different words).
Implications: Re-emphasize importance.
Future Work: Based on the limitations (in 6.3), what can you or the community do next in the coming years? (Example: Expand to other problems, optimize energy…).

Auxiliary Sections

Acknowledgements: Who funded the money? Which unit lent the lab/GPU? Who contributed ideas but not enough to be an author?
References: Correctly cite every claim (statement) in the above sections.
Appendix: Long mathematical proofs, extremely large data tables, or code snippets that would dilute the reading flow if placed in the main text.