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FWIW: I personally don't think a scaling "wall" is conclusive, but I do think that the signal-to-noise ratio in language >> images >>> proteins >> DNA. So the LLM / "compressor" should be more intentional, esp. since mutation assays look at such remarkably finegrained details

I think we started using transformers in ~2019 because protein and DNA also have global and local patterns, like language. Maybe somewhere along the way we forgot that some biological tasks don’t neatly fit self-attention’s strengths for and are mostly local patterns (for ex.,

Deadline is on May 26 AoE!! Best paper awards are awarded for each track (incl. the AI for Science track) ✏️🤖🧪🗺️

Our Rising Stars series is back with Paula Nicoleta Gradu sharing a control-theoretic perspective on medication tapering problems! Link: youtu.be/IP9unMmvbNE?si…

It’s finally happening!!! Diffusion is so much more satisfying than autoregressive for protein & DNA sequences that don’t really have directionality 🥹 Waiting for this to empirically land & replace BERT/one-step discrete diffusion for protein foundation models 👀

Flash Invariant Point Attention １．FlashIPA introduces a linear-scaling reformulation of Invariant Point Attention (IPA), a core algorithm in protein and RNA structure modeling. It achieves SE(3)-invariant geometry-aware attention with dramatically reduced memory and runtime,

also to be clear!! some of the best academic works in discrete diffusion have been for proteins/molecules/DNA. Point here is Google/Inception etc fully investing in this could bear more fruit than at first glance Also this idea of scratch pad / baked-in error correction /

Over the past year, I've been compiling some "alchemist's notes" on deep learning. Right now it covers basic optimization, architectures, and generative models. Focus is on learnability -- each page has nice graphics and an end-to-end implementation. notes.kvfrans.com

Submission deadline is now **May 31 AoE**! Best Paper awards are given for the robotics, RL theory, language modeling, and AI for Science tracks. Exploration is an evolving and expanding scope of research -- excited to explore (ha ha) the intersections together 🗺️🧭🤖🧪

ditto on thanks to all co-authors 😊 check out our work in Patterns, a Cell Press journal!

MFW people are surprised that scaling up transformer-based protein language models didn't help with hella high-resolution variant effect fitness prediction tasks

Tokenization is just a special case of "chunking" - building low-level data into high-level abstractions - which is in turn fundamental to intelligence. Our new architecture, which enables hierarchical *dynamic chunking*, is not only tokenizer-free, but simply scales better.

Thrilled to finally release this study! 🚀 We view (discrete) diffusion models as implicitly doing data augmentation over autoregressive. Through this lens, we find that diffusion outperforms AR in data-constrained settings, but it requires larger models and way more epochs to

Data diversity, quality & relevance rules over model size any day of the week. Very clever approach of generating synthetic protein sequences from backbone structures to give big boosts to pLMs.