Ayhan Suleymanzade

@ayhozade

PhD student at @EPFL; prev AITHYRA, CS at KAIST; LLM/continuous/flow reasoning; geometric deep learning

Lausanne, Switzerland misakitaro0414.github.io Joined May 2023

653 Following 474 Followers 119 Posts

Latest posts 5

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Ayhan Suleymanzade @ayhozade Sep 11

Can neural networks learn to think by denoising?

Excited to share Thinking with Looped Flows!

We train recurrent reasoning with local denoising objectives. It is a new approach for reasoning that achieves SoTA on ARC-AGI-1 and 2 among looped models.

In collaboration with @chandavidlee, @FEijkelboom, @nmboffi, @ismaililkanc, @jw9730.

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Ayhan Suleymanzade @ayhozade Oct 21

Happy to see the use of looped flows in science 🧬 :) Great work @DidiKieran!

Kieran Didi Verified account @DidiKieran Oct 21

@ArashVahdat @karsten_kreis @tomasgeffner @nscorley @RafiBrent @maxkazman We also found things that echo recent flow language model work. We replaced our long-term enemy, self conditioning (@osclsd), with recurrent updates, which others concurrently presented as looped flows (@nmboffi). These “thinking modes” boost performance further. 11/30

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Ayhan Suleymanzade @ayhozade Sep 24

MUX has been accepted to #NeurIPS2026 as a Spotlight! 🎉

Ayhan Suleymanzade @ayhozade Jun 3

Can LLMs reason in superposition? We introduce MUX, a method that turns text CoT into latent continuous reasoning.

Instead of one-hot vectors as in CoT, the model now learns to predict weighted averages of several one-hot vectors, that we call multiplexed tokens. These multiplexed tokens can be designed to be lossless, so by predicting them one is essentially doing multi-token prediction (MTP) in superposition.

MUX is the best latent reasoning method across 32 math settings spanning 1-8B LLaMA base models, reducing CoT length by 3-6x. Furthermore, it is able to perform parallel search, harnessing a core strength of superposed reasoning.

In collaboration with @alperen_gozeten , @mmbronstein, @ismaililkanc, and @jw9730.

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Ayhan Suleymanzade @ayhozade Sep 18

100% recommended!

ELLIS @ELLISforEurope Sep 18

🏹 Job alert: Fully Funded PhD Positions in Machine Learning & AI for Science

📍 Vienna 🇦🇹
⏰ 1 Nov
🔗 bit.ly/4ipJUyD
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Ayhan Suleymanzade @ayhozade Jun 3

Can LLMs reason in superposition? We introduce MUX, a method that turns text CoT into latent continuous reasoning.

Instead of one-hot vectors as in CoT, the model now learns to predict weighted averages of several one-hot vectors, that we call multiplexed tokens. These multiplexed tokens can be designed to be lossless, so by predicting them one is essentially doing multi-token prediction (MTP) in superposition.

MUX is the best latent reasoning method across 32 math settings spanning 1-8B LLaMA base models, reducing CoT length by 3-6x. Furthermore, it is able to perform parallel search, harnessing a core strength of superposed reasoning.

In collaboration with @alperen_gozeten , @mmbronstein, @ismaililkanc, and @jw9730.

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