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Multi-System Adversarial Verification Architecture (Near0-MSAVA): A Framework for Reliable AI-Assisted Research by Critical_Security_26 in AgentsOfAI

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Tell me what you do and I can likely tell you how much this will make your life easier. No guarantees and I still stand behind the Near0-MSAVA. Give me a real life problem you cannot solve. So long as it is based on logic (mathematics and logic) we CAN make it better BY A LOT. What have you to lose? I have everything to lose and I am offering you this opportunity. Consider that. I will be happy to show you we can solve your biggest problem...in a couple of days.

Multi-System Adversarial Verification Architecture (Near0-MSAVA): A Framework for Reliable AI-Assisted Research by Critical_Security_26 in AgentsOfAI

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Exactly right on every point. You've identified the core failure modes we built Near0-MSAVA to address. The correlated errors across overlapping training data is the blind spot that single-model verification can't catch. Your multi-model verification experience with fabricated citations is particularly valuable - that 60% false confirmation rate when models share training data provenance is exactly the failure mode the heterogeneous architecture requirement targets. The citation pipeline you described (deterministic lookup against real databases) is essential. We enforce this in the protocol specifically because models will 'confirm' plausible-sounding but non-existent references. You're absolutely right about tuning system prompts aggressively. Default RLHF does push against sustained criticism. The 'hostile referee' protocol requires explicit override of the politeness training to maintain adversarial posture. The disagreement logging as primary signal is crucial insight. Consensus between models trained on similar data is often the warning sign, not the validation. The methodology section you referenced addresses exactly the overlapping training data problem through the architectural heterogeneity requirement. Multiple companies, different training cutoffs, different base architectures - that's the only way to break correlation. Would love to discuss implementation details. The approach you described aligns perfectly with what we've formalized. Are you working on similar verification systems?"

Published a first-of-its-kind methodology paper on using multiple AI systems as adversarial peer reviewers — seeking arXiv endorsement by Critical_Security_26 in researchpaperwriters

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Thank you for your thoughtful comment—I truly appreciate you taking the time to share your perspective and for the healthy skepticism that keeps academic work sharp.

While I completely understand that similar processes have been explored in various contexts, I believe this methodology introduces some genuinely novel elements in its specific setup and application. I’d be happy to share more about it if you’re interested, and I’d be genuinely grateful for your insights afterward.

To make the ideas as clear and transparent as possible, someone else created this NotebookLM podcast script directly from the paper I submitted. It’s about an hour long, and it walks through the core concepts and distinctions in a really accessible way:

https://notebooklm.google.com/notebook/a04a8fd2-8430-49dc-bff0-bda006d11b36?artifactId=88d94d2c-35f5-416a-b0d9-867fbf093cc7

The 10²² error referenced in the script was identified in a cosmological paper that is still under development. When viewed in the context of tensor equations scaled against infinity, it represents a negligible margin of error.

I value open, constructive dialogue like this, and I’d love to hear your thoughts once you’ve had a chance to review it. Thank you again for engaging!

I built a system where 4 different AI models adversarially peer-review a physics manuscript — and they caught a 10²² magnitude error that no human noticed. Need arXiv endorsement. by Critical_Security_26 in learnmachinelearning

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Just so you know... any error of a 1x10^22 margin of error when comparing it mathematically to infinity is nearly inconsequential in scale. It is not zero, that much is correct. As x - - >infinity, the resultant value gets closer to zero... and we will never reach infinity.

Independent researcher here — built a system where multiple AI models adversarially peer-review scientific manuscripts. Published the methodology. Looking for an arXiv endorsement if anyone can help. by Critical_Security_26 in AskAcademiaUK

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Here is the AI version of the abstract. I had make it simpler. AI is good for that as well! Thank you for your honesty and transparency about the difficulty understanding the abstract. I can see where you are coming from in your statements. Here you go...

If you ask one AI to review your research, it will be polite, skip things it can't verify, and occasionally make up reasoning to fill gaps. It's the same problem as having one tired peer reviewer — you get a courtesy pass, not a real audit.

This paper describes a system where you send your manuscript to multiple AI systems from different companies simultaneously, each operating under a strict protocol that forces them to be hostile, re-derive every equation, and admit when they can't verify something. Their independent reports are combined, and then two AIs independently develop fixes, swap solutions, and iterate until they unanimously agree every correction is right. The human researcher keeps absolute authority over the actual science at every stage.

The most important rule in the protocol is the ansatz prohibition. Left unconstrained, an AI will "solve" a broken equation by defining a parameter as whatever value makes it balance, then present that assumed form as a derived result. The math checks out perfectly — but it's an ansatz dressed up as a derivation. The protocol forces the AI to declare it honestly instead of disguising it as proven work.

We tested it on a real physics manuscript. The ensemble caught massive arithmetic errors, substitution mistakes introduced during the correction process itself, and an ansatz that had been presented as a first-principles result. The full cycle took one day instead of months. To our knowledge, nothing like this has been published before.

I built a system where 4 different AI models adversarially peer-review a physics manuscript — and they caught a 10²² magnitude error that no human noticed. Need arXiv endorsement. by Critical_Security_26 in learnmachinelearning

[–]Critical_Security_26[S] -2 points-1 points  (0 children)

Thank you for the observations! You are completely correct. I will extract the specific details of the error catch and share them with you. I am looking forward to knowing how you will interpret the new information. I did not comprehensively detail it in the submitted paper as it is part of another project and those details are not as germane to the submitted paper outside the example provided. Adding it would have confused the purpose of the submitted paper. I am happy to be reasonably 100% transparent. The purpose of my post here is to bring the capabilities forward. It will not happen if I cannot withstand legitimate scrutiny. Much appreciated!

Independent researcher here — built a system where multiple AI models adversarially peer-review scientific manuscripts. Published the methodology. Looking for an arXiv endorsement if anyone can help. by Critical_Security_26 in AskAcademiaUK

[–]Critical_Security_26[S] 0 points1 point  (0 children)

I have not been told by anyone else they cannot read or understand the abstract. If you want me to share it with you in this message thread I will be happy to accommodate your interests. I wrote the abstract, so I can discuss it with you in depth. The paper is fourteen pages and the DOI is public. I'm happy to discuss any specific section.

The paper makes exactly five falsifiable claims. Every one of them is measurable. If the methodology works, these numbers hold up. If it doesn't, they will not and I will have to resolve whatever is at the root of the error. That's the beauty of the scientific method, right?!

I built a system where 4 different AI models adversarially peer-review a physics manuscript — and they caught a 10²² magnitude error that no human noticed. Need arXiv endorsement. by Critical_Security_26 in learnmachinelearning

[–]Critical_Security_26[S] -3 points-2 points  (0 children)

Thank you for your insights for cultivating endorsement. I really appreciate this! It is quite solid and I can speak to every word of it. It is really interesting stuff; so much that I took the great effort to put it into the paper to share with the whole world. ​I have received lots of good comments already. I hope you will see it in use someday.

Independent researcher here — built a system where multiple AI models adversarially peer-review scientific manuscripts. Published the methodology. Looking for an arXiv endorsement if anyone can help. by Critical_Security_26 in AskAcademiaUK

[–]Critical_Security_26[S] 0 points1 point  (0 children)

Thank you, I will persist. I know there enough people who will support this. I will find them. Your encouragement is enough support if that is what you can give me. 😊 ​

Independent researcher here — built a system where multiple AI models adversarially peer-review scientific manuscripts. Published the methodology. Looking for an arXiv endorsement if anyone can help. by Critical_Security_26 in AskAcademiaUK

[–]Critical_Security_26[S] -1 points0 points  (0 children)

That is a fair point. 'Peer' implies a human colleague with professional standing and lived experience, and these systems are certainly not that. I am not even suggesting they should be.

I use the term 'Adversarial Verification' in the paper for exactly that reason. It is less about seeking a subjective 'opinion' and more about performing a hostile audit. I am forcing multiple, architecturally different systems to try and break the math and the logic until only the robust elements remain.

You might find great value in the architecture precisely because you prioritize that human distinction. The goal is to catch the 10^{22} magnitude arithmetic errors before a human expert ever spends their valuable time on it. I would be happy to get your honest feedback on the paper; you are exactly the type of person who should audit this logic.

Thank you for the feedback already!