Skip to content
Apart Research
Apart Research
Contact us
Sprint projectMar 22, 2026San Franciso

Grant Trust System

Shon Pan, Caleb Strom · Team ARI

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Grant Trust System

Open in a new tab (opens in new tab)
Recording (opens in new tab)More on grantrustsys-production.up.railway.app (opens in new tab)
Share

This is the grant system of an effort to create a significantly more streamlined and automated research method that still centers around human intent. The basic idea is to use a system of costly signals as well as intentionality signs to filter out "AI slop" grant applications, and use it as a form of control to reduce misuse.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Costly real-world signals like shipped projects and open-source contributions provide adversarial robustness that text-only monitoring cannot is an interesting idea worth developing further. The system architecture is clearly documented and the red team attack surface analysis in Appendix D shows good adversarial thinking about how each layer could be defeated.

    The main concern is fit with the AI control hackathon theme. The system addresses AI-generated text detection and grant quality assurance rather than the adversarial monitoring settings. On methodology, the four synthetic test cases constructed by the authors demonstrate the system's design logic but can't establish generalisation, and validating against a labelled corpus of real grant applications with ground-truth oversight levels, as noted in the paper, would be the essential next step.

Cite this project

@misc{pan2026grant,
  title = {{Grant Trust System}},
  author = {Shon Pan and Caleb Strom},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/grant-trust-system-3sdg}},
  url = {https://apartresearch.com/sprints/projects/grant-trust-system-3sdg}
}

More from AI Control Hackathon 2026

All 125 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026