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Aurko Roy
113 posts
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Aurko Roy
@aurko79
Math & computer science | @AIatMeta (2025-2025) | @GoogleDeepmind (2023-2025) | @GoogleAI (Brain) (2017-2023) | CS PhD @Georgiatech | CS @IITKanpur
San Francisco
scholar.google.com/citations?user…
Joined February 2025
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  • Pinned
    user avatar
    Aurko Roy
    @aurko79
    Jul 4, 2025
    Excited to share what I worked on during my time at Meta. - We introduce a Triton-accelerated Transformer with *2-simplicial attention*—a tri-linear generalization of dot-product attention - We show how to adapt RoPE to tri-linear forms - We show 2-simplicial attention scales
    149K
  • user avatar
    Aurko Roy
    @aurko79
    Oct 23, 2025
    Who would have thought that a multi trillion dollar cap company could have been thrown into such chaos (layoffs) by a single technical decision they made a year ago - using expert choice MoEs for their frontier model.
    256K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 18, 2025
    Got nerd sniped into checking out karpathy's nanoGPT github, I made the following changes to run 2-simplicial attention on my mac on Shakespeare: - 6 layers, 6 heads, 384 dim - reduced ctxt len to 32 - 2-simplicial attention with 32 x 32 x 32 window - run for 5000 steps
    30K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 7, 2025
    Last week at Meta - looking back on the last 3 months I spent there, feel lucky to have worked with some amazing folks: @vinaysrao, @saanarkethayan, @_t_chou, @__yjc_, @_arohan_, @agarwl_, @brandfonbrener, @afrozenator, @dvsaisurya, @manzilzaheer Excited for what's next!
    23K
  • user avatar
    Aurko Roy
    @aurko79
    Oct 17, 2025
    Really nice extension of 2-simplicial attention from sliding window local attention to content based sparse attention! Bonus: Also draws a nice connection to the Weisfeiler Leman algorithm, which I last had the occasion to think about 13 years ago for my master's thesis. :)
    user avatar
    tensorqt
    @tensorqt
    Oct 16, 2025
    we can go beyond attention. as some of you know, higher-order attention methods (and the resulting schizodrawings) have been my focus for a while now, and, despite my earlier plans, they ended up being my choice for the second post in the series titled "the graph side of
    11K
  • user avatar
    Aurko Roy
    @aurko79
    Sep 5, 2025
    Amazing work by the Pytorch team!
    user avatar
    PyTorch
    @PyTorch
    Sep 5, 2025
    FlashAttention in 3D? Our latest blog explores the #kernel design of 2-Simplicial #Attention, modeling the algorithm with a hardware aligned design and rewriting the entire kernel in TLX (Triton Low Level Extensions). 🔗 hubs.la/Q03H6S9D0 #PyTorch #OpenSourceAI
    6.8K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 4, 2025
    Replying to @aurko79
    Paper link:
    arXiv logo
    arxiv.org
    Fast and Simplex: 2-Simplicial Attention in Triton
    Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size and token count...
    5.8K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 4, 2025
    Replying to @giffmana
    Hanging out with @_arohan_ in SF
    6K
  • user avatar
    Aurko Roy
    @aurko79
    Aug 6, 2025
    Insight I had yesterday talking to someone: inference time compute is a way to scale attention FLOPs over FFN flops, since the ratio between them is n^2d/(nd^2) = n/d. In inference time scaling n grows while d remains fixed.
    2.8K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 18, 2025
    Replying to @keveman
    Code snippet and efficient triton kernels are in our paper
    arXiv logo
    arxiv.org
    Fast and Simplex: 2-Simplicial Attention in Triton
    Recent work has shown that training loss scales as a power law with both model size and the number of tokens, and that achieving compute-optimal models requires scaling model size and token count...
    1.5K
  • user avatar
    Aurko Roy
    @aurko79
    Jul 4, 2025
    Replying to @aurko79
    Special shout-out to @_t_chou for some amazing work on triton kernels!
    4.9K
  • user avatar
    Aurko Roy
    @aurko79
    Sep 17, 2025
    Had the pleasure of grabbing a beer with the inspiring @danielmurfet and talking about attention, intelligence, math and Grothendieck at Berkeley this weekend! 🍻
    1.8K
  • user avatar
    Aurko Roy
    @aurko79
    Sep 19, 2025
    Agreed, this is why we worked on 2-simplicial attention and OSSed the kernels: Paper: arxiv.org/abs/2507.02754 Blog post: pytorch.org/blog/fast-2-si…
    user avatar
    Percy Liang
    Together AI
    @percyliang
    Sep 19, 2025
    -2016 (classic era): focus on data efficiency 2017-2025 (pretraining era): focus on compute efficiency 2026-: focus on data efficiency (again) The standard Transformer paradigm is optimized for compute efficiency. As we look at data efficiency, we'll see very different design
    4K
  • user avatar
    Aurko Roy
    @aurko79
    Oct 23, 2025
    Replying to @suchenzang
    Chunked attention aka Long range arena (LRA) "local attention" from github.com/google-researc… @msaffar3 @_arohan_ @ashVaswani and I spent many days trying to figure out how local attention could be worse than some of the methods listed there.
    GitHub - google-research/long-range-arena: Long Range Arena for Benchmarking Efficient Transformers
    From github.com
    6.2K