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Super excited to be at @iclr_conf in Rio! I'll be presenting "Topological Flow Matching" in collaboration with the amazing @ismaililkanc and @AlexanderTong7. We improve flow matching performance for modelling signals on graphs and simplicial complexes by aligning sample paths with heat diffusion. Find out more at the poster! 🗓️ Friday, April 24, 2026 ​🕒 3:15 PM - 5:45 PM BRT ​📍 Pavilion 3 · Poster P3-#820

New episode of The Information Bottleneck is out, this time with @liuzhuang1234 (Princeton). We talked about ConvNeXt and whether architecture still matters; dataset bias and what "good data" actually looks like; ImageBind and why vision is the natural bridge across modalities; CLIP's blind spots; memory as the real bottleneck behind the agent hype; whether LLMs have world models; and Transformers Without Normalization. For years, the vision community debated what actually matters: architecture, inductive bias, self-attention vs convolution. After a lot of back-and-forth, we ended up in a funny place: ViT and ConvNet give roughly the same performance once you tune the details. What I find interesting is that once you reach a certain performance level, it becomes much easier to swap and tweak components without really changing the outcome. Talking to Zhuang on this episode, I kept wondering whether the same is now true for LLMs. If we wil spent serious time on an alternative architecture today, would you actually get a meaningfully different model, or just land on the same Pareto curve with extra steps? I'm starting to suspect it's the latter. Architecture matters less than we think. Data, compute, and a handful of pillars do most of the work.