🙏 CVPR is a wrap for the MHUG lab! A huge thank you to everyone who stopped by our posters, asked questions, and shared ideas. It’s been an incredible few days. See you next year! 👋 #CVPR2026 More pics from our posters 👇

🚀 CVPR, here we come! The MHUG team is bringing a massive lineup of 17 papers this year, and we couldn't be more excited to share what we've been building! 🧠💻 🧵👇

Excited to present our #NeurIPS2025 paper on "λ-Orthogonality Regularization for Compatible Representation Learning" today! 🎉 Come chat with me at Poster #2503 in Exhibit Halls C, D, E 📅 Fri, Dec 5 | 🕚 11am – 2pm PST #MachineLearning #AI #Compatibility #RepresentationLearning

λ-Orthogonality: a tiny near-orthogonal adapter with a controllable λ "knob" to balance geometry preservation and adaptation. Enables backward-compatible embeddings (no full backfill) with near new-model performance and <50% gallery updates. @NeurIPSConf #NeurIPS2025

Encouraging to see compatibility discussed in this NeurIPS workshop. The theoretical understanding of the problem’s structure is still limited, with room for useful progress. @NeurIPSConf #NeurIPS2025 sites.google.com/view/ccfm-n…

LeJEPA what else

New in-depth blog post - "Inside vLLM: Anatomy of a High-Throughput LLM Inference System". Probably the most in depth explanation of how LLM inference engines and vLLM in particular work! Took me a while to get this level of understanding of the codebase and then to write up this one - i quickly realized i understimated the effort. 😅 It could have easily been a book/booklet (lol). I covered: * Basics of inference engine flow (input/output request processing, scheduling, paged attention, continuous batching) * "Advanced" stuff: chunked prefill, prefix caching, guided decoding (grammar-constrained FSM), speculative decoding, disaggregated P/D * Scaling up: going from smaller LMs that can be hosted on a single GPU all the way to trillion params (via TP/PP/SP) -> multi-GPU, multi-node setup * Serving the model on the web: going from offline deployment to multiple API servers, load balancing, DP coordinator, multiple engines setup :) * Measuring perf of inference systems (latency (ttft, itl, e2e, tpot), throughput) and GPU perf roofline model Lots of examples, lots of visuals! --- I realize i've been silent on social - many of you noticed and thanks for reaching out! :) --> I'm so back! lots of things happened. Also, in general, I'm a bit sick of superficial content, it really is an equivalent of junk food (h/t @karpathy). I want to do the best/deepest technical work of my life over the next years and write much more in depth (high quality organic food ;)) so I might not be as frequent around here as i used to be (? we'll see). I'll make it a goal to share a few paper summaries a week or stuff that's relevant / in the zeitgeist. If you have any topics that happened over the past few weeks/months drop it down in the comments i might focus on some of those in my next posts. --- Huge thank you to @Hyperstackcloud for giving me an H100 node to run some of the experiments and analysis that i needed to write this up. The team there led by Christopher Starkey is amazing! Also a big thank you to Nick Hill (who did a very thorough review of the post - basically a code review lol; Nick's a core vLLM contributor and principal SWE at RedHat) and to my friends Kyle Krannen (NVIDIA Dynamo), @marksaroufim (PyTorch), and @ashVaswani (goat) for taking the time during weekend when they didn't have to!

Calling all AI researchers & comic lovers! 🚀 Can your model pick the right comic panel? 🤖 Join the #ICDAR2025 Comics Understanding Competition & submit your results by 15/04/2025! 📆 Challenge: rrc.cvc.uab.es/?ch=31 Dataset: huggingface.co/datasets/VLR-… Submissions are open!

Are you a professional working on video in Milan ? Come to the Milan Video Tech Meetup this Thursday (Nov. 7, 6:00 PM CET) at the NTTData headquarters, Via Ernesto Calindri, 4. I'll talk about how to use GenAI to improve the performance of video codecs. meetup.com/milan-video-tech-…