We need more ways to raise public awareness re: the risks of rogue ASI.

Mox is the community infrastructure that do-gooders in SF need. In particular, I want to recognize the superb career transition advising and EA SF events by Jen Ba!

It's a small world, I've worked with Gergo at Amplify, heard exciting things about SAIGE already, reside in Germany and found that I still have some funds on this platform, so donating a tiny amount to show my support.

Approved! Getting adoption for something like this seems like a clear win, and we're excited about the team.

As of August 2026 (the last time I updated this post), we needed $50k to make it to end of 2026. Since then, Daniel Brockman generously donated $50k, and we're keeping our same budget for the rest of 2026! The numbers in this post are now up to date as of Sep 3, 2026. Note that the total donation amount shown at the top of this Manifund page is cumulative since 2025, and doesn't include Daniel Brockman or a few other large donations received outside this platform. Our Substack subscription revenue is currently $4,700/year. I've updated the post to mention that, which was my oversight before.

If at any point we run out of money, the show must go on! Worst case, in the form of me singlehandedly producing fewer episodes at lower quality and landing fewer high-quality guests. But it'd be a shame to lose momentum on this project when there's a huge opportunity to make a difference.

Canceled the pending offers here! The grantee wrote: "I've completed the work this grant would have funded, but secured a better funding route for it, so I won't be accepting this grant. Please regrant these funds, or if necessary return them to the funder. Kindly treat this grant as withdrawn and closed on my end"

Quick Update: Correcting a Mathematical Term in v1 (Reduced vs. Non-reduced Schemes) & The Road to v2

I want to address a very sharp and helpful feedback I received regarding the algebraic properties of our $2 \times 2$ linear attention singularity model in the pre-print.

In the draft, I described the rank-1 collapse subscheme $S_{\text{collapse}} = \text{Spec}(\mathbb{R}[x, y, z, w] / \langle xy - z w \rangle)$ as a "non-reduced" scheme containing nilpotent elements.

As a math-rigor check, the commenter is 100% correct: since $xy - zw$ is an irreducible polynomial in a UFD, the generated ideal $I = \langle xy - zw \rangle$ is prime. Therefore, the coordinate ring is an integral domain, making the scheme reduced (meaning it contains no nilpotents). Describing the $2 \times 2$ toy model as non-reduced was a technical misnomer on my part, and I will correct this wording in the next revision (v2) on Zenodo.

Does this affect the core claims of the AGEP Theorem? Absolutely not. The core mathematical proof of Theorem 4.1 (the algebraic restoration of the Donsker property) and our PyTorch simulation results remain completely untouched and robust. The monoidal blow-up still successfully regularizes the KL divergence and the Jacobian into normal crossing monomials, and the Dudley entropy integral still beautifully converges on the exceptional divisor.

Why this feedback actually accelerates our roadmap: This correction does not weaken our theory; rather, it provides the perfect mathematical bridge to our next generalized phase.

In real-world deep learning, we do encounter true non-reduced structures. The severe "learning plateaus" (flat regions where gradients collapse) are best modeled as nilpotents. As suggested, by extending our ideal to non-reduced forms such as $I = \langle (xy - zw)^2 \rangle$ (representing quadratic flatness near the singularity) or $I = \langle xy - zw, x^2 \rangle$ (representing isolated nilpotent "spikes" when certain weights die), we can mathematically describe the dynamic of SGD getting stuck in plateaus.

I am extremely grateful for this high-level feedback. It has already sharpened our focus for Work Package 1 (WP1), and we are now formulating the "Non-Reduced AGEP Theorem" for v2 to directly capture these flat plateaus.

Thank you for watching and supporting our journey! I will keep you posted as we refine the draft.

Very interested in getting Japan's AI safety talent funnel up and running! The budget here is also very modest for the impact.

I am a big believer in Lawrence and the team at BASE. Their mission is very important to the future of AI safety and the broader community's ability to leverage talent, ideas, and viewpoints from under-represented and geographically diverse populations. Without greater inclusivity and this type of representation in AIS, I worry that the community will generate safety research and solutions which are limited in scope, viability, and impact globally. For these reasons I have volunteered to be a Mentor for the Cohort 2 Fellowship, and also support this specific initiative.

@CJSmith Saya melihat komentar Anda dan ketertarikan Anda pada pendekatan yang benar-benar baru. Saya sedang mengembangkan arsitektur yang membangun integritas internal AI. Anda bisa mampir melihat proyek saya disini, saya hanya sekedar ingin tahu apakah menurut Anda ini termasuk pendekatan baru yang juga Anda maksud dari perspektif anda.

Thanks for the endorsement guys @morisil @bitowaqr.

@Austin thanks for your acknowledgement via reactions ✨

As an AI researcher affiliated with the Foresight Institute I highly recommend a grant for this project.

Great work – much needed! Foresight Institute endorsement.

Our first quarterly report is out: https://separatrix.ai/research/quarterly-report-2026-08-31/

To be frank a lot of the past two months has gone into logistics and spinning up, but I am proud of the progress we are making. I hope and expect to post more frequent updates this quarter.

@alawondetheophilus17 Absolutely. When public risk communication is connected to governance telemetry, it moves from passive awareness to actionable public oversight. Looking forward to seeing where you take this research.

@Emmanuel-king, yes. Risk communication for public preparedness should not be isolated from other interventions and pursuits such as broader AI safety pursuits, AI governance, and even AI-related policymaking.

@CJSmith, I don't disagree with the premise. However, I'd say it still boils down to a lack of risk communication for public preparedness. Ideally, AI risk communication should be such that also provides transparent information regarding AI capabilities, since the capabilities form a part of the risk factors (although other elements such as misalignment would still need to be tied into the equation).

@CJSmith I agree. I also think preparedness becomes much stronger when people can verify what AI systems actually did, not just understand what they're capable of. Better risk communication and better governance mechanisms should reinforce each other.

@Emmanuel-king, thank you for the support.

This makes a lot of sense. We need more approaches that are totally novel like this, rather than incremental attempts at change that continually fail.

People don't understand capabilities so they can't prepare for harm. That needs to change.

We need more ways to raise public awareness re: the risks of rogue ASI.

Mox is the community infrastructure that do-gooders in SF need. In particular, I want to recognize the superb career transition advising and EA SF events by Jen Ba!

It's a small world, I've worked with Gergo at Amplify, heard exciting things about SAIGE already, reside in Germany and found that I still have some funds on this platform, so donating a tiny amount to show my support.

Approved! Getting adoption for something like this seems like a clear win, and we're excited about the team.

As of August 2026 (the last time I updated this post), we needed $50k to make it to end of 2026. Since then, Daniel Brockman generously donated $50k, and we're keeping our same budget for the rest of 2026! The numbers in this post are now up to date as of Sep 3, 2026. Note that the total donation amount shown at the top of this Manifund page is cumulative since 2025, and doesn't include Daniel Brockman or a few other large donations received outside this platform. Our Substack subscription revenue is currently $4,700/year. I've updated the post to mention that, which was my oversight before.

If at any point we run out of money, the show must go on! Worst case, in the form of me singlehandedly producing fewer episodes at lower quality and landing fewer high-quality guests. But it'd be a shame to lose momentum on this project when there's a huge opportunity to make a difference.

Canceled the pending offers here! The grantee wrote: "I've completed the work this grant would have funded, but secured a better funding route for it, so I won't be accepting this grant. Please regrant these funds, or if necessary return them to the funder. Kindly treat this grant as withdrawn and closed on my end"

Quick Update: Correcting a Mathematical Term in v1 (Reduced vs. Non-reduced Schemes) & The Road to v2

I want to address a very sharp and helpful feedback I received regarding the algebraic properties of our $2 \times 2$ linear attention singularity model in the pre-print.

In the draft, I described the rank-1 collapse subscheme $S_{\text{collapse}} = \text{Spec}(\mathbb{R}[x, y, z, w] / \langle xy - z w \rangle)$ as a "non-reduced" scheme containing nilpotent elements.

As a math-rigor check, the commenter is 100% correct: since $xy - zw$ is an irreducible polynomial in a UFD, the generated ideal $I = \langle xy - zw \rangle$ is prime. Therefore, the coordinate ring is an integral domain, making the scheme reduced (meaning it contains no nilpotents). Describing the $2 \times 2$ toy model as non-reduced was a technical misnomer on my part, and I will correct this wording in the next revision (v2) on Zenodo.

Does this affect the core claims of the AGEP Theorem? Absolutely not. The core mathematical proof of Theorem 4.1 (the algebraic restoration of the Donsker property) and our PyTorch simulation results remain completely untouched and robust. The monoidal blow-up still successfully regularizes the KL divergence and the Jacobian into normal crossing monomials, and the Dudley entropy integral still beautifully converges on the exceptional divisor.

Why this feedback actually accelerates our roadmap: This correction does not weaken our theory; rather, it provides the perfect mathematical bridge to our next generalized phase.

In real-world deep learning, we do encounter true non-reduced structures. The severe "learning plateaus" (flat regions where gradients collapse) are best modeled as nilpotents. As suggested, by extending our ideal to non-reduced forms such as $I = \langle (xy - zw)^2 \rangle$ (representing quadratic flatness near the singularity) or $I = \langle xy - zw, x^2 \rangle$ (representing isolated nilpotent "spikes" when certain weights die), we can mathematically describe the dynamic of SGD getting stuck in plateaus.

I am extremely grateful for this high-level feedback. It has already sharpened our focus for Work Package 1 (WP1), and we are now formulating the "Non-Reduced AGEP Theorem" for v2 to directly capture these flat plateaus.

Thank you for watching and supporting our journey! I will keep you posted as we refine the draft.

Very interested in getting Japan's AI safety talent funnel up and running! The budget here is also very modest for the impact.

I am a big believer in Lawrence and the team at BASE. Their mission is very important to the future of AI safety and the broader community's ability to leverage talent, ideas, and viewpoints from under-represented and geographically diverse populations. Without greater inclusivity and this type of representation in AIS, I worry that the community will generate safety research and solutions which are limited in scope, viability, and impact globally. For these reasons I have volunteered to be a Mentor for the Cohort 2 Fellowship, and also support this specific initiative.

@CJSmith Saya melihat komentar Anda dan ketertarikan Anda pada pendekatan yang benar-benar baru. Saya sedang mengembangkan arsitektur yang membangun integritas internal AI. Anda bisa mampir melihat proyek saya disini, saya hanya sekedar ingin tahu apakah menurut Anda ini termasuk pendekatan baru yang juga Anda maksud dari perspektif anda.

Thanks for the endorsement guys @morisil @bitowaqr.

@Austin thanks for your acknowledgement via reactions ✨

As an AI researcher affiliated with the Foresight Institute I highly recommend a grant for this project.

Great work – much needed! Foresight Institute endorsement.

Our first quarterly report is out: https://separatrix.ai/research/quarterly-report-2026-08-31/

To be frank a lot of the past two months has gone into logistics and spinning up, but I am proud of the progress we are making. I hope and expect to post more frequent updates this quarter.

@alawondetheophilus17 Absolutely. When public risk communication is connected to governance telemetry, it moves from passive awareness to actionable public oversight. Looking forward to seeing where you take this research.

@Emmanuel-king, yes. Risk communication for public preparedness should not be isolated from other interventions and pursuits such as broader AI safety pursuits, AI governance, and even AI-related policymaking.

@CJSmith, I don't disagree with the premise. However, I'd say it still boils down to a lack of risk communication for public preparedness. Ideally, AI risk communication should be such that also provides transparent information regarding AI capabilities, since the capabilities form a part of the risk factors (although other elements such as misalignment would still need to be tied into the equation).

@CJSmith I agree. I also think preparedness becomes much stronger when people can verify what AI systems actually did, not just understand what they're capable of. Better risk communication and better governance mechanisms should reinforce each other.

@Emmanuel-king, thank you for the support.

This makes a lot of sense. We need more approaches that are totally novel like this, rather than incremental attempts at change that continually fail.

People don't understand capabilities so they can't prepare for harm. That needs to change.