There's a temptation in research to wait until the evidence feels bulletproof, one more benchmark, one more experiment, one more round of validation, before sharing your work. But that instinct, however understandable, can quietly bury the most compelling part of your contribution. The question you're really asking isn't whether you have enough data. It's whether you trust your own proof enough to let others see where it leads.
You have something rare: a formal mathematical guarantee of convergence for a novel agentic system. That's not a footnote. That's the foundation. Most papers in this space offer empirical results with all the caveats that come from noisy, messy, hard-to-reproduce experiments. You've done the harder thing, you've shown that your system *must* converge under defined conditions. That's a result that stands on its own, independent of how many examples you can run today. The fact that you've paired it with a real-world application, even just a couple of examples, gives reviewers something concrete to anchor to. That's not a weakness. That's a direction.
The real problem you've identified is that no existing benchmark captures the complexity of your actual use case. Synthetic data feels hollow. Off-the-shelf benchmarks miss the point. So you're stuck between publishing something that feels thin or waiting indefinitely for a dataset that may never materialize. But here's the thing: the research community doesn't just need more results. It needs more proofs that are actually exercised in the wild, even if the wild is small. A couple of well-chosen examples, documented honestly and analyzed rigorously, can be more persuasive than a hundred synthetic runs that no one believes. Reviewers are trained to spot overclaiming. They're also trained to recognize when someone has done the theoretical work and is willing to show their hands early.
What you should do is submit. Not because the paper is finished, it's not, and it doesn't have to be. But because the combination of formal proof and a real-world anchor is exactly the kind of contribution NeurIPS exists to surface. The feedback you'll get, even if it's harsh, will sharpen your thinking about what evidence matters most. And if you're worried about being scooped, remember that the proof is yours. No one else has it. No one else can write it the way you can. The risk isn't in submitting too early. It's in holding onto a strong idea until the moment it feels safe, and watching someone else move the field forward without you.
So, yes, submit. But don't submit the version that apologizes for its small empirical footprint. Submit the version that leads with the proof, frames the real-world examples as early evidence, and explicitly asks the community for help in building better benchmarks. That's not a weakness. That's a contribution. And it's one that only you can make.