The continual learning and post-transformer architecture research lab behind the path to AGI.
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We presented ResonatorLM at ICANN this afternoon.
We think long-context language modeling and continual learning need a different architecture, and that is the problem ResonatorLM is working on.
If that is the kind of work you care about, our blog is at axioniclabs.ai/research/reso…
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axioniclabs retweeted
Our account was temporarily compromised, We will NEVER launch a token!!!!
There is no Axionic Labs token or crypto affiliation. We will never launch a token. Any and all tokens launched under the Axionic name are fake and a scam. Our Twitter was temporarily hacked. Please be careful!
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There is no Axionic Labs token or crypto affiliation. We will never launch a token. Any and all tokens launched under the Axionic name are fake and a scam. Our Twitter was temporarily hacked. Please be careful!
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@axioniclabs research is being published at ICANN 2026. Our research on post-transformer alternatives will be highlighted in the main conference from 14–17 September at ICANN, held at University of Padua
ResonatorLM explores an alternative to self-attention for long-context sequence modelling, using learned resonators to build a more efficient recurrent architecture.
At @axioniclabs, we work on new model architectures and learning systems that challenge the assumptions behind today's AI stack.
ICANN brings together researchers working on the foundations of neural networks. That is the hardest audience this work could have, which is exactly why we want it in front of them.
Come talk to us if you're working on post-transformer architectures, continual learning or adaptive AI models.
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we are in LA on 26 september.
HOSTLINE. founders, researchers, infra people, and the investors backing them.
saturday, 6:30pm, private estate on the west side. open bar, DJ, food.
list is by request. address comes with the approval.
dress code is all white and it is enforced.
partiful.com/e/gxhFNw2n4JKLn…
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25 founders, a wood-panelled room on van ness, and one question: when does it make sense to train your own model?
founders dinner at harris' with @thundercompute came in with a thesis, left with a better question.
nyc next, sept 9 with @crossmint:
luma.com/wvghia9s
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Excited to see our good friends @ThunderCompute raise their series A!
Today, we raised a $13M series A to solve the GPU shortage
Trillions are being spent on GPU CapEx while 80% of it sits idle
The capacity already exists
Virtualizing GPUs unlocks it
Backed by Matrix, YC, and CEAS
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axioniclabs retweeted
Today, we raised a $13M series A to solve the GPU shortage
Trillions are being spent on GPU CapEx while 80% of it sits idle
The capacity already exists
Virtualizing GPUs unlocks it
Backed by Matrix, YC, and CEAS
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Your AI model is too smart for its own good.
Bigger isn't better. It's just more expensive.
On Aug 20 in SF, we're hosting a founders dinner with @ThunderCompute on why task-specific models beat general-purpose ones on cost, speed, and accuracy.
Luma link soon!
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Your AI model is too smart for its own good.
Bigger isn't better. It's just more expensive.
On Aug 20 in SF, we're hosting a founders dinner with @ThunderCompute on why task-specific models beat general-purpose ones on cost, speed, and accuracy.
Luma link soon!
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We’re hiring our Founding Designer at Axionic(Backed by Entrepreneurs First)
You’ll shape how Axionic Labs looks, feels, and shows up from day one.
Strong taste. Big ownership. Blank canvas.
Full JD in the comments. 📩 navya@axioniclabs.ai
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We recently announced a new paper, centered around a post-transformer architecture designed for language modeling. A 🧵follows:
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We are not the first to explore this architecture: independent researchers have proposed a physics-based alternative to traditional transformers in forums such as HuggingFace and Reddit. However, we are the first to formalize it.
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