Re-engineering the Research Paper for the AI Era

The academic engineering paper, a cornerstone of scientific discourse, is in the midst of a profound, albeit quiet, identity crisis. Its primary author is increasingly an AI, and its most diligent reader is also an AI. Large Language Models (LLMs) now assist in drafting everything from literature reviews to methodology sections, while other algorithms parse, summarize, and categorize these papers for databases and discovery tools. In this new ecosystem, the traditional paper format, designed for human contemplation, has become an inefficient bottleneck. We are, in essence, forcing AIs to write verbose prose for other AIs to then painstakingly deconstruct. The time has come to redesign the research paper to be what it is now becoming: a machine-readable, data-first document.

Should you enjoy the writing? (Created with Gemini)

With that, the current model seems extremely wasteful, especially when environmental costs of training and using LLMs come to mind. It encourages "algorithmic bloat", long-winded introductions, meandering literature reviews, and detailed descriptions of standard procedures, that serves neither human nor machine. A human researcher skims for the core contribution, while an AI reader must sift through semantic filler to be able to extract the same key information. This existing model inadvertently created (already) a culture of "citation race," where publications resemble a self-referential game of citation counts rather than a genuine pursuit of knowledge. Researchers often cite their own previous work or tangentially related papers, inflating bibliography length without adding substantive context. This practice, combined with the publication of "minimal publishable units" often designed to train students or secure grants, clutters the academic record with articles that offer little novel insight. Furthermore, the traditional prose-heavy structure, coupled with a lack of accessible data and the inability to readily verify cited sources (often due to paywalls), makes it exceedingly difficult for human reviewers to detect logical fallacies or unsubstantiated claims.

Here and now, we have a choice: either continue following these inefficient procedures to report research progress, or rethink that process, for example by looking into the conciseness of medical research, that prioritizes clarity, efficiency, and, as a result, machine-parsability. 

A. The Introduction is the Contribution 

The lengthy background and historical context should be eliminated unless they are foundational to the paper’s novel claim. The paper should begin immediately with a clear, structured statement of its contributions. The "why" should be self-evident from the problem being solved.

S. Context via Structure, Not Prose. 

The sprawling literature review is obsolete. It should be replaced by a structured table of prior work and existing gaps. This table can list key preceding works, their methods, their limitations, and precisely how the current paper differs or improves upon them. This is more direct for a human reader and immediately parsable for an AI. We all create such tables while doing the research, but when we start writing the paper, we revert to prose.

H. Methodology as a Pointer 

Describing a well-established process in detail is redundant. If the methodology is not a core contribution, it should be reduced to a methodology names and citations. For example:

Methodology: We employed a standard Adam algorithm (Diederik P. Kingma, Jimmy Lei Ba, 2014) with and standard decay rates  (β1​=0.9, β2​=0.999) and a learning rate of 1×10−4.

I. Data and Code as First-Class Citizens 

To ensure true transparency and reproducibility, the paper must treat its data and code as core components, not as optional supplements. All datasets, models, and analysis scripts must be made available in persistent, publicly accessible repositories (e.g., Zenodo, Figshare, GitHub). These must be linked directly within the paper. This is a non-negotiable component. A claim without accessible data is an unsubstantiated assertion. This allows any reader, human or AI, to immediately access and interrogate the evidence, transforming the paper from a static report into a dynamic, verifiable research object.

F. Explicit, Parsable Contribution Sections 

The paper's core innovations must be explicitly declared in a dedicated, machine-readable section using simple tags or a structured data block (e.g., JSON-LD):

• <Contribution type="dataset">We introduce a new benchmark dataset of 5,000 annotated synthetic images of brain scans.</Contribution>

• <Contribution type="methodology">We propose a novel behavioral mechanism, "Cognitive Restructuring," that reduces driving anxiety by 36%.</Contribution>

T. Short, Data-Driven Results and Conclusions 

The results section should be a direct presentation of data: figures, tables, and key metrics. The conclusion should be a bulleted list summarizing the findings and their implications, mirroring the claims made in the contribution section.

The Role of the Human Reviewer in the AI Era

This shift logically raises a critical question: How can such a condensed, hyper-specialized paper be reviewed? The answer is not to eliminate human oversight, but to empower it. Human review remains the most critical defense against fraud, ethical abuse, and the injection of pseudoscience into the scientific record.

Citation or not citation?  (Created with Gemini)

In this new model, the reviewer’s role becomes that of an expert arbiter of integrity and logic, facilitated by AI. Upon receiving a paper, the reviewer gains access to a suite of tools. One tool, using the paper’s structured citations, generates a traditional, narrative-style introduction and literature review on demand, providing the necessary context for reviewers who are not directly involved in the area explored by the authors.

Crucially, with mandatory data and code linking, the reviewer can also deploy automated analytical and replication tools. These tools can run directly on the provided dataset to independently replicate key figures, perform standardized statistical checks, and flag anomalies or inconsistencies in the data. This empowers the reviewer to move beyond trusting the author’s presentation to actively verifying the findings.

Freed from parsing prose and empowered with analytical tools, the human reviewer can focus on higher-order tasks:

• Verifying Integrity - Do the results presented logically follow from the methodology? Do the findings from the replication tools match the author's claims?

• Assessing Soundness - Is there any evidence of data manipulation, p-hacking, or other forms of academic dishonesty flagged by the analysis?

• Evaluating Significance - Does the contribution, now clearly isolated and verified, represent a meaningful advance over the prior art?

• Ethical Oversight - Are there any foreseeable ethical implications or potential misuses of the technology that need to be addressed?

This "human-in-the-loop" model preserves rigorous, critical oversight while adapting to the realities of an AI-driven research landscape. Important, described approach is not something new. This is how research papers are structured and reviewed in some discipline, and, how they were envisioned to be. With rapid AI proliferation in our life, we have a chance to fix current bugs and level up the process all together. Will we?

The first question we must ask ourselves is: Is current system of writing based on tradition is corrupted to the degree that it is impeding new discovery?

The second question is: Is AI ruining academic writing as in its traditional form?

?Have you answered YES to both questions...