May funding radar + a citation verification workflow for grant proposals.

What: The Department of Transportation opened its FY26 SBIR pre-solicitation period, running through May 29. Full submissions are expected to open June 3, with proposals due July 7.

So what: DOT is one of the smaller SBIR agencies, with fewer applicants per topic and less competition than DoD or NIH. If your technology addresses transportation safety, infrastructure, autonomous vehicles, or mobility, it's worth checking whether the topics align. The narrower topic set means fewer opportunities, but also a less crowded field.

Do: Review the pre-release topics. If there's a fit, you have about five weeks from open to deadline. How to apply.

What: NSF announced X-Labs, a new initiative funding independent research organizations (not universities) with large, multiyear awards. The mechanism is Other Transactions Agreements, not standard grants. First topics: Scientific Instrumentation for Sensing and Imaging, and Quantum Systems (Interconnects and Integrated Photonics). Total commitment: up to $1.5B over the next decade, with additional topics expected in the coming weeks.

So what: This is a different model from SBIR. Bigger awards, longer timelines, milestone-driven, and designed for entrepreneurial teams pursuing platform-level scientific capabilities. If you've been building outside the university system and SBIR felt too small or too short for your work, X-Labs may be a better fit. The Sensing and Imaging topic is broad enough to cover a range of instrumentation approaches.

Do: Read the funding opportunity on SAM.gov and register for the introductory webinar at the NSF X-Labs page. Future topic announcements are expected soon.

What: NIH published official guidance on AI use in grant applications, with a focus on fabricated references. Their position: citations pointing to nonexistent papers can constitute data fabrication under research misconduct rules. Key statement: "Applications that are either substantially developed by AI or contain sections substantially developed by AI are not considered the original ideas of applicants and will not be considered by NIH." NSF separately updated its misconduct definition to explicitly include AI tools. A peer-reviewed paper (Resnik & Hosseini, 2026) provides the legal analysis: fabricated citations can meet the federal misconduct standard when the researcher acts with "recklessness": indifference to a known risk.

So what: If improper AI use is detected after an award, NIH may refer the case to the Office of Research Integrity (ORI). But this is also relevant beyond AI: citation errors affect proposal quality regardless of their source. A 2025 meta-analysis found roughly 17% of citations in peer-reviewed medical publications contain inaccuracies, about half of them major. The error rate in proposals, where no editor verifies references, is likely higher.

Do: Build citation verification into your proposal workflow as a distinct step - the practical approach is below.

New to SBIR? Every abbreviation in this checklist is explained in plain English in our SBIR Glossary.

A biotech PI is preparing an NIH R43 application. Sixty-plus references across Specific Aims, Research Strategy, and the background section. Assembled from a reference manager, a colleague's shared library, and a recent literature search. The references were added incrementally as the proposal developed. Some were carried forward from a previous submission. Some were added late in the process to support a reviewer's likely question.

This is how most proposal reference lists come together: incrementally, from multiple sources, formatted toward the end of the writing period.

Why treat citation verification as a formal quality gate rather than part of the final proofread?

Reviewer credibility. A reviewer who recognizes an incorrect publication year, a misspelled author name, or a citation that doesn't support the claim you're making will question the rigor of the rest of your proposal. This has always been true.

Compliance risk. NIH and NSF have published updated policies on fabricated references. Under the current framework, a researcher who submits a proposal containing ghost references and who didn't verify them could meet the standard for "recklessness": acting with indifference to a known risk of fabrication. This applies whether the fabrication was intentional or the result of using tools (including AI) without checking the output.

Error baseline. If 17% of citations in peer-reviewed medical papers contain errors (Baethge & Jergas, 2025) - and those are papers that went through editorial review, the rate in proposals, where no editor checks references, is likely higher. Most of these errors are preventable with a systematic pass.

A useful framework from Resnik & Hosseini (2026) distinguishes three categories:

1. Ghost references - the citation doesn't exist

A fabricated author, title, journal, or DOI. This is the highest-risk category under the updated NIH and NSF standards, as it can constitute data fabrication. AI tools are known to produce these - plausible-looking citations that combine real author names with invented titles - but they also arise from garbled notes, misremembered papers, or copying citations from secondary sources without verification.

2. Wrong metadata - real paper, incorrect details

The paper exists, but the publication year is wrong, an author name is misspelled, the journal title is incorrect, or the DOI doesn't resolve. This won't trigger a misconduct investigation, but it signals a lack of attention to detail. Reviewers who know the field will notice.

3. Misattributed claims - real paper, doesn't support the assertion

The paper exists and the metadata is correct, but it doesn't actually say what you're citing it for. This is the most common category, with or without AI involvement. It typically happens when you cite based on an abstract without reading the relevant section, or when you cite a paper for a claim it mentions in passing rather than as a finding.