I just recently submitted my first preprint to arXiv as an independent researcher, and the process has been labyrinthine yet also straight forward. Each individual step has been fairly easy to understand and complete, but understanding which steps I actually need to go through took some time.

I see all the time from researchers in the field how they get these cold emails that basically are like "I'm a random person who figured out AGI, hit me up". My goal was not to have a conversation with some big name, it was to write and submit a paper on the experiments I ran, so I didn't approach it that way. Here's the steps that I went through:

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Step 1: Conduct the experiment and collect data that is reproducible

This should be obvious, but a lot of "independent researchers" apparently do not understand this, so I will say it plainly: it is not worth time or effort for an established researcher to engage with your ideas unless you have data to describe, characterize, limit, and support your ideas.

You should not be sending any emails without actual data. I didn't send any emails until I had a nearly finished paper.

Step 2: Attempt to break your experiment

I had data that appeared to support my claims. So I tried to break the experiment. I tried to look for confounds. I adjusted the design of the experiment to account for confounds. I reran the experiment and recollected data. I updated the code to reduce sources of uncertainty and ended up writing an experiment framework that guarantees bit-for-bit reproducibility on the same hardware.

For this step, I heavily relied on and consulted with existing AI models. Some of the confounds I identified on my own, some they identified. I had the AI's write ablation scripts and test scripts so I could prove to myself that the fixes actually controlled for the things I wanted them to.

The results held up, on to the next step.

Step 3: Make sense of your data

This was the beginning of the paper writing process. I collected a LOT of data. (Over 100 different metrics throughout the training.) So I used AI tools to help write data analysis tools that would help me surface the most important metrics. This actually resulted in a few additional discoveries that were buried in my data.

Step 4: Outline the paper

I created an outline of what the paper should cover, without specific references, data, or anything like that. Essentially a structured description of what the paper should be saying.

Step 5: Write the paper

This was an iterative process that involved (for me) working with Claude Opus to slowly turn my outline into a fleshed out paper. I made notes on specific things that should be included. I hand wrote most of the first draft. At this point, the paper included no figures, no tables, and no specific references to the collected data, it was more general.

Step 6: Refine the paper

For this, I wrote a simple MCP server that allowed the AI I was doing revisions with (Claude) to query the raw data. I gave it the task of finding metrics in the data that should be surfaced into the paper directly based on the paper that had been written. This resulted in substantial rewriting, because the text of the paper had to change a lot to accommodate the actual metrics.

Step 7: Literature review for a non-academic researcher

At this point, I did some initial searches on my own. I looked up terms that I thought were relevant, and found papers that I felt might be important to cite. Then I used the following process:

• Upload the paper in its current state to the "Deep Research" mode equivalent for Gemini, Claude, and GPT.
• Give it the task of validating that the citations I identified are relevant.
• Give it the additional task of identifying obvious citations that SHOULD be used but aren't in that list.

Using all three models helped improve confidence and coverage for me. Some citations were things that all three models identified as missing. Those seemed like obvious inclusions. Some were extremely well argued by only one of the models, which let me ask the other two models what they thought about including that citation. This helped prevent hallucinated citations.

Then I compiled a final list of proposed citations and asked all three models to provide me the information necessary for me to check the citation myself. The way I did this was by requiring the AI to provide me either a direct link to the cited paper, OR provide me with a link to ANOTHER paper that has the same citation so I could verify the citation information there.

Then I actually read (mostly skimmed) every single citation. For the ones that seemed central to my paper, I actually read them in depth.

Then I came back to Claude with my list of verified and selected citations, and asked it to help me integrate the citations into the paper. This also involved substantial rewriting.

Step 8: Finish rough draft

At this point, I have something that resembles an actual scientific paper. I identified what metrics would be most important to visualize and created figures from the raw data. I integrated those into the paper.

Then I went through around 5-10 passes of reading the ENTIRE paper and making edits and adjustments. Most of those were made by hand instead of working with an AI tool.

Step 9: AI Review

The next step for me was to export the entire paper as a PDF and provide it to all three models I was working with again, but this time I gave it a prompt that asked it to address these points:

• Does this paper make a genuine contribution, or does it merely dress up an expected result?
• Does this paper make appropriate citations, or are the conspicuous missing, incorrect, or misused citations?
• If it makes a genuine contribution, is that contribution novel or is it merely an extension of other work?
• Are the methods described in enough detail and specificity that it is possible to reproduce from the paper alone?
• Is the paper written in a way that is likely to be understood by others?
• Does the paper make claims that are in line with the results and findings? When/If it does make claims or assertions that extend beyond immediate results, are these qualified properly, or is the motivation and justification for doing so clear?

I did this, again, using the "Deep Research" modes of each model. This surface several additional blindspots, and actually resulted in additional experimental redesign, data collection, and nearly a complete rewrite of the paper. I think I cycled through steps 1-9 about 6 or 7 times.

Step 10: The cold emails

NOW is the point where I sent emails to people active in the field. I decided that the most reasonable way to do this was probably to email the lead authors of papers which are cited in my paper and are central to the claims I was making. In my case, there were several existing papers that my results appeared to either explain mechanistically/theoretically, or that my paper extended into other domains.

This resulted in me contacting Jaerin Lee, Andrey Gromov, Evan Hubinger, Neel Nanda, and Christoper De Sa. I wrote emails that were structured as so:

"I have recently conducted an experiment that [way that it is relevant to their research]. I found that [the finding that is relevant to their research].

[A general description of the experiment and topline results.]

If you have time and interest, I would appreciate getting your thoughts or feedback on this part of my paper. I have the paper, code, and data available to send if you are interested.

I don't work as a researcher, and came to this result independently.

Would you be willing to take a look, or point me to someone who you think would be more appropriate to contact?"

I got replies from two of the people I contacted, both of which engaged with the results themselves and asked to be sent the actual paper. This resulted in approximately a month of sparse email exchanges where they both:

1. Asked questions that showed the actually read at least parts of the paper.
2. Asked questions that I did not have answers for because they were not ideas or questions I had thought of.
3. Provided suggestions on other papers that I should look at as being potentially relevant to my paper.

This was despite the fact that at the point they both reviewed the paper, it was nearly 45 pages long including appendices. The main body was nearly 30 pages long.

Step 11: Further iteration from professional feedback

Both people who replied, as I mentioned, asked questions that I didn't have answers for. Some of these questions were not relevant to my specific claims and work, but some were. So, over the next month I actually wrote small little tools to answer some of those questions and shared my results back with them. Some of this iteration actually made it into the paper too.

Step 12: Final draft for preprint

At this point, I went through and cleaned things up to remove any "rough draft" type artifacts. I made sure that all of the figures and direct metrics in the paper matched the "gold data" that was going to release with the paper. I went through it with a fine-toothed comb, basically.

Step 13: Requested arXiv endorsement

I directly asked the two people who replied if they would be willing to provide an arXiv category endorsement so that I could publish the preprint and potentially get broader feedback. One of them said that they would, and within a few days, my arXiv account was allowed to submit to the cs.LG category on arXiv.

Step 14: Prepared external artifacts and repositories.

I cleaned up and prepared the GitHub repos that would be referenced in the paper for other people to inspect. This included a README describing exactly how to reproduce the paper results.

Step 15: Submitted on arXiv

Finally, I submitted the paper on arXiv. After about two days of waiting/review, it was announced on March 27th.

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Now I'm looking into how I would prepare the paper for submission to NeurIPS 2026. For me, that will be fairly challenging, because the main body of the paper is 32 pages and I'm uncertain how I could reduce it to 9 pages without making the core claims incomprehensible. But, just in case I figure that out, I'm in the process of getting an independent researcher profile on OpenReview. That process can take weeks.

I didn't actually begin this entire process with the intent to write a paper. I was just running a little personal experiment to learn something, and produced a result that seemed important.

But, for anyone else doing independent research, I would say:

1. Have at least a rough draft of a paper written, including the data for it, BEFORE contacting other researchers.
2. Look into all the specific requirements, accounts, verifications, etc. that you will need to submit/publish the paper.
3. Figure out your "announcement strategy" before actually submitting. I submitted the paper but had no established presence here or in any other ML research discussion space, so I don't think basically anyone has actually seen it yet. It doesn't matter how amazing your paper or research is, people won't engage with it if they don't know it's there.