The Scientific Homogenization Problem: Does AI Make Research Proposals Too Similar?

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Large language models can help researchers express ideas more clearly, overcome language barriers, and spend less time formatting grant applications. But they may also create a subtler systemic risk: scientific proposals could become increasingly similar to one another—and increasingly similar to research that funding agencies have already supported.

A 2026 preprint examining US federal research funding found that greater estimated LLM involvement was consistently associated with lower semantic distinctiveness in National Science Foundation (NSF) and National Institutes of Health (NIH) proposals and awards. Proposals with stronger LLM traces were positioned closer, in semantic space, to work funded by the same agency during the preceding year.

This does not prove that AI directly suppresses scientific originality. The study is observational, and its authors explicitly avoid making a causal claim. Nevertheless, it identifies a serious portfolio-level problem: even when individual AI-assisted proposals remain scientifically valid, widespread use of similar models may gradually steer research toward a narrower conceptual center.

What Is Scientific Homogenization?

Scientific homogenization is the reduction of meaningful diversity among research questions, methods, explanations, or proposed directions.

It does not necessarily mean that proposals contain identical sentences. Two applications may use different vocabulary while making nearly the same assumptions, following the same established agenda, or positioning themselves relative to the same fashionable literature.

Homogenization can occur at several levels:

• Linguistic homogenization: proposals use similar phrases, structures, transitions, and persuasive language.
• Conceptual homogenization: researchers frame different problems through similar theories or methodological templates.
• Strategic homogenization: applicants converge on topics they believe reviewers and funding agencies are most likely to approve.
• Portfolio homogenization: the collection of funded projects becomes concentrated around established directions rather than spanning high-risk and unconventional alternatives.

The last form is the most important. Science does not require every individual proposal to be radically novel. It does, however, require a sufficiently diverse portfolio so that failures in one dominant theory, technology, or research program do not stall an entire field.

What the 2026 Preprint Found

The preprint, The Rise of Large Language Models and the Direction and Impact of US Federal Research Funding, was released in January 2026 by Yifan Qian, Zhe Wen, Alexander C. Furnas, Yue Bai, Erzhuo Shao, and Dashun Wang.

The researchers combined:

• confidential funded, unfunded, and pending NSF and NIH proposals from two large US research universities;
• publicly available NSF and NIH award records;
• grant-publication links used to study subsequent research output.

They estimated the degree of LLM involvement from textual patterns in proposal abstracts. They then measured semantic distinctiveness using SPECTER2 scientific-text embeddings. Each abstract was compared with projects funded by the same agency during the previous year.

The central result was consistent across four datasets: private NSF submissions, private NIH submissions, public NSF awards, and public NIH awards.

Greater estimated LLM involvement was associated with proposals being semantically closer to recently funded research.

For public NSF awards, moving from relatively low to relatively high LLM involvement corresponded to an approximately five-percentile decline in semantic distinctiveness. For NIH awards, the decline was approximately four percentile points.

The models included controls for the year, research field, funding amount, and investigators. Because investigator fixed effects were included, the analysis partly compared researchers with themselves: an investigator’s proposals with stronger LLM involvement tended to be less distinctive than that investigator’s proposals with weaker involvement.

That makes the result more informative than a simple comparison between frequent AI users and non-users. However, it still does not establish causation.

Is This Merely Similar Writing?

One possible explanation is that LLMs simply make prose sound alike. Expressions such as “this project seeks to address,” “a significant unmet need,” and “the proposed framework will enable” may appear frequently even when the underlying ideas differ.

The study attempted to test this explanation. The researchers took 6,000 NSF and NIH abstracts from 2021 and used an LLM to rewrite them while preserving their scientific content. The rewritten abstracts changed substantially in language and style, but their measured semantic positions were nearly indistinguishable from those of the originals.

This robustness test suggests that the main result cannot be explained entirely by superficial stylistic smoothing.

It still does not prove that an LLM changed the research idea itself. Researchers may choose to use AI more extensively when preparing conventional proposals, when targeting familiar programs, or when trying to align an application with previously successful work. The observed relationship could therefore reflect both AI behavior and applicant strategy.

Why LLMs Could Pull Proposals Toward the Center

Large language models generate text by learning statistical patterns from enormous collections of existing material. When asked to improve a proposal, a model is generally rewarded for producing language that is coherent, plausible, complete, and recognizable.

Those properties are valuable—but they are not identical to originality.

A model asked to “make this research proposal more compelling” may predict what a compelling proposal normally looks like. It can introduce familiar motivations, standard impact claims, conventional methods, and expected connections to prior literature. The result may become easier to evaluate while losing some of the unusual structure that distinguished the original idea.

Several mechanisms can produce convergence.

Models Prefer High-Probability Continuations

An LLM normally selects from patterns that are well represented in its training data. Unless deliberately instructed otherwise, it may replace an unusual but meaningful formulation with a more statistically typical one.

Researchers Use Similar Prompts

Thousands of applicants may issue nearly interchangeable instructions:

• improve clarity;
• make the proposal persuasive;
• emphasize novelty;
• align the text with the call;
• identify broader impacts;
• anticipate reviewer objections.

Even when the underlying projects differ, common prompts can impose a common rhetorical and conceptual architecture.

AI Learns the Language of Previously Funded Science

Public grant abstracts, journal articles, institutional guidance, and successful proposal examples may all influence the language represented in model training or retrieval systems. An AI assistant can therefore become an indirect imitation engine for established funding patterns.

Reviewers Reward Familiar Legibility

Homogenization is not solely a model problem. Applicants already adapt proposals to reviewer expectations. AI makes that adaptation faster and more systematic.

If conventional framing improves evaluation outcomes, researchers have an incentive to use AI to make unconventional ideas appear more conventional. Eventually, the funding system may select for proposals that occupy a narrow zone of optimized familiarity.

Does AI Assistance Improve Funding Success?

The 2026 study found different patterns at the NSF and NIH.

For NSF submissions, estimated LLM involvement was not significantly associated with funding success. For NIH submissions, moving from relatively low to relatively high LLM involvement was associated with an approximately four-percentage-point increase in funding probability.

Among NIH awards, greater LLM involvement was also associated with roughly 5% more subsequent publications. However, the additional output was concentrated among ordinary publications rather than papers in the top citation percentiles. The study covered only the first one or two years after award, so it could not determine long-term scientific impact.

A separate 2026 European Commission Joint Research Centre working paper examined business applications to Horizon Europe. It estimated that around 40% of proposal abstracts contained LLM-modified content by the end of 2024. Extensive LLM involvement was correlated with lower scores and funding probabilities in cross-sectional comparisons, but repeated-submission analysis did not show that adopting LLM assistance itself caused worse evaluations.

Together, these studies suggest that there is no universal “AI advantage” or “AI penalty.” Effects depend on the funding agency, applicant population, degree of assistance, evaluation criteria, and way AI involvement is measured.

Why Less Distinctive Does Not Automatically Mean Worse

Semantic distinctiveness is not a complete measure of scientific quality.

A proposal may resemble existing work because it:

• replicates an important result;
• applies a validated method to a neglected population;
• fills a precise gap in a cumulative research program;
• develops infrastructure needed by many related projects;
• responds directly to an urgent public-health problem.

Conversely, a highly distinctive proposal may be original because it is poorly grounded, irrelevant, or technically unsound.

Funding agencies should therefore not maximize novelty blindly. They should distinguish productive cumulative research from portfolio-level intellectual concentration.

The danger appears when funding decisions repeatedly favor proposals close to the recent center without reserving sufficient resources for replication, alternative paradigms, foundational work, and high-uncertainty exploration.

How Funders Can Preserve Research Diversity

Prohibiting LLMs would be difficult to enforce and would remove legitimate benefits, particularly for researchers writing in a second language or working without institutional grant-writing support. A better approach is to redesign evaluation and portfolio management.

Evaluate the Underlying Contribution Separately from the Prose

Review forms should ask reviewers to identify:

• the exact new claim or capability;
• the main departure from prior work;
• assumptions inherited from the dominant literature;
• plausible alternative approaches;
• the value of the project even if its central hypothesis fails.

This shifts attention from rhetorical polish toward scientific substance.

Measure Portfolio Diversity

Agencies can use embedding-based analysis to detect excessive thematic clustering across submitted and funded proposals. Such measurements should inform human judgment rather than automatically reject individual applications.

A portfolio dashboard could identify:

• overfunded conceptual clusters;
• neglected topics;
• methodological monocultures;
• repeated dependence on the same datasets;
• unusually original proposals requiring specialist review.

Protect High-Variance Research

A portion of funding should be explicitly reserved for projects that are difficult to compare with recent awards. These applications may require different review criteria, longer evaluation horizons, or smaller exploratory grants before full-scale funding.

Require Meaningful AI Disclosure

A simple checkbox asking whether AI was used provides little information. More useful disclosure would distinguish among:

• grammar and translation assistance;
• structural editing;
• literature discovery;
• generation of scientific arguments;
• formulation of hypotheses or methods;
• production of evaluation or impact claims.

Disclosure should not automatically penalize applicants. Its purpose should be auditing and policy research.

Use Multiple Models and Adversarial Review

Reliance on one model family can create a common intellectual bottleneck. Agencies and researchers can reduce this effect by comparing outputs from different systems and asking an adversarial reviewer—human or machine—to identify where a proposal has become generic.

The objective is not to make every sentence unusual. It is to preserve the researcher’s actual distinctions.

Implications for AI-Based Research Funding

AI can also be used on the evaluation side of funding. This creates both an opportunity and a compounding risk.

An AI evaluator could detect overlooked value, compare proposals across disciplines, reduce administrative costs, and help independent researchers receive consideration. But if applicants and evaluators rely on similar models, the entire funding pipeline may become a feedback loop:

1. Models learn from established scientific literature and funded projects.
2. Applicants use those models to optimize new proposals.
3. Evaluation systems favor proposals legible within the same learned patterns.
4. Funded projects become future training and comparison data.
5. The conceptual center becomes progressively more dominant.

AI funding systems therefore require explicit safeguards for novelty, minority hypotheses, and research that cannot yet be described using established terminology. This is part of the broader problem of AI alignment in scientific funding: the system must optimize for the long-term public value of science rather than merely predicting which applications resemble previously successful ones.

Projects exploring automated research funding, including World Science DAO, should treat semantic diversity as a governance objective. An AI should not distribute money solely by assigning an isolated score to each proposal. It should also evaluate how each project changes the composition and risk profile of the total research portfolio.

AI Should Clarify Original Ideas, Not Replace Them

The current evidence does not justify the claim that LLMs inevitably make science unoriginal. It does justify a narrower warning:

When many researchers use statistically similar systems to optimize proposals against similar institutional expectations, individually rational assistance can produce collective scientific convergence.

The practical distinction is between using AI as an editor of independently developed ideas and using it as a generator of fundable scientific positioning.

Researchers can reduce homogenization by drafting the central hypothesis, conceptual distinctions, and methodological commitments before consulting an LLM. They can then instruct the model to preserve unusual terminology where it carries technical meaning, flag generic substitutions, and identify where its revisions move the proposal toward conventional assumptions.

The scientific homogenization problem is not a reason to abandon AI. It is a reason to stop treating polished language as a neutral layer placed over research.

Proposal writing influences how ideas are classified, compared, and selected. When AI changes that layer at scale, it may also change the direction of science. Funding institutions should begin measuring that effect now—before semantic convergence becomes an invisible norm.