TheoryOfEverything.ai Beta Is Live

Today marks the public beta launch of TheoryOfEverything.ai.

We went public with the founder video, shared the announcement on social media, and started opening the platform to researchers beyond the first private previews. A few new subscribers have already come in through the demo and early preview paths, which is exactly what this phase is for: real researchers, real submissions, real review feedback, and fast iteration.

This beta is not a blank landing page with a promise attached. The review system is live. The calibration work is public. The first author videos are up. The subscription tiers are open. And the platform now has enough trust, transparency, and documentation around it that we can invite people in with a straight face.

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The New Launch Videos

The most visible change this week is video.

The new founder video, "Why TheoryOfEverything.ai Exists," explains the mission behind the platform: rigorous scientific review should not require institutional affiliation, and independent researchers deserve feedback that is structured, transparent, and serious.

We also now have author walkthroughs from our first featured researchers:

Blake Shatto walks through Mode Identity Theory, a framework built from a single topological postulate that claims to derive the cosmological constant, fermion masses, and more across 122 orders of magnitude. His framework scored A 4.3/5 on the platform, and his testimonial is one of the clearest examples of why this exists: the review caught a wording issue that would have made a mathematical claim invalid while the work was already with editors.

John Holland introduces General Expanse Tension Theory, backed by 45 papers on Zenodo and decades of independent research extending the Standard Model. John has also been an active evangelist for the project and one of the people pushing us to make the platform useful to serious independent authors.

You can watch all three on the new Videos page.

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What Shipped Before Beta

The public launch comes after a busy build week. The biggest pieces now live:

The calibration study is complete. We ran a preregistered 4-phase study covering tier discrimination, repeatability, prompt-version stability, and model-family bias isolation. The short version: better papers score higher, repeat reviews are stable, prompt v2.2 is stricter without breaking rank order, and the panel does not simply favor its own model family. The full methodology and data are public at /calibration.

The strongest validation story is now written. Our system independently flagged the same missing quantum potential term that a physicist later identified in a formal arXiv rebuttal to a Royal Society paper. The flawed paper scored 2/5; the rebuttal scored 4/5 Approved. That story matters because it shows the platform can discriminate between prestige and mathematical validity. Read the full story.

Mathematical Risk Flags now ship with reviews. When a math specialist sees a compressed derivation, an unverified equation step, or a claim whose downstream conclusions depend on a fragile proof step, the review can surface that as an equation-level warning. These flags are visible to readers without pretending that every concern should become a score penalty.

The agent API is live. Researchers can create drafts, submit for review, poll for completion, retrieve review feedback, search their own work and published community work, and publish approved submissions through MCP-compatible tools.

Author control improved. Conceptual-track submissions no longer auto-publish just because they were reviewed. Authors now choose when below-threshold work becomes publicly visible, which is the right default for early-stage theory work.

Trust and transparency got a public pass. The Terms page now has a plain-language summary, the About page has a stronger conflict-of-interest and provenance disclosure, and first-time reviewers see a pre-submit confirmation explaining specialist agents, providers, timestamps, and withdrawal.

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What Beta Means

Beta means the core loop is ready for real use:

• Submit a paper or framework.
• Get a structured multi-agent review across scientific rigor dimensions.
• See specialist feedback, mathematical flags, and a coordinator synthesis.
• Improve the work and re-review.
• Publish when you are ready, or keep iterating privately.
• Download an endorsement packet or share a review profile when the work is strong enough.

There will still be rough edges. We expect that. But the basic promise is working: an independent researcher can bring a serious theory to the platform and get a structured, paradigm-neutral review that is much closer to a real scientific critique than a generic AI chat response.

That is the bar for this beta.

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For New Subscribers

If you subscribed through the demo or received an early preview invite: thank you. You are helping turn this from a calibrated system into a real research workflow.

The best next step is simple: submit one piece of work that would genuinely benefit from structured review. It can be a polished paper, a framework draft, a supporting derivation, or a piece you are not ready to publish yet. The system is most useful when you use the first review as a map, not a verdict.

If you are not ready to submit, watch the videos, read the calibration page, and look at the public review examples. The goal is not to make the platform mysterious. The goal is to make the review process inspectable.

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What Comes Next

The next phase is outreach and feedback.

We will keep tightening the trust pages, publishing calibration stories, improving the author journey, and listening closely to the first beta researchers. The platform is especially interested in independent physics frameworks, mathematical derivations, preprints, and long-running research programs that need more than a thumbs-up or thumbs-down.

If that sounds like your work, start with the demo, watch the videos, or create an account and submit something for review.

The beta is live.

Now we find out what independent researchers can do with it.

Questions? Ideas? Hit the feedback button in your dashboard or email adam@theoryofeverything.ai.

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TOEShare uses independent specialist AI agents from multiple providers with coordinator synthesis to produce structured, paradigm-neutral review of scientific work. Learn more at /about or see the full calibration methodology at /calibration.