🔮At #ICML2026 we presented Attentive Multi-Layer Fusion for Vision Transformers, an efficient method for decoding information from multiple layers of ViTs for downstream task predictions. It’s almost as performant as full fine-tuning but only an inch more expensive than linear probing!

ExplainableML@eml-munich.bsky.social · last mo.

8/ ALF: Attentive Multi-Layer Fusion for ViTs @lciernik.bsky.social , Marco Morik, @zeynepakata.bsky.social , @lukasmut.bsky.social [Paper] openreview.net/forum?id=wSXZtvqwoa [Code] github.com/lciernik/attentive-layer-fusion

2/ Wed, Jul 8, 2026 • 10:30 AM – 12:15 PM KST · HALL A #2711 · POSTER Attentive Multi-Layer Fusion for Vision Transformers @lciernik.bsky.social , Marco Morik, @zeynepakata.bsky.social , @lukasmut.bsky.social Paper: arxiv.org/abs/2601.09322 Code: github.com/lciernik/att...

Beyond the final layer: Attentive multilayer fusion for vision transformers

With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a lightweight head, is ...

arxiv.org

Can context-sensitivity improve human–machine alignment in VFMs? We show that incorporating context improves modeling of human similarity decisions. 🎉Accepted to ReAlign ICLR! Now on @arxiv: arxiv.org/abs/2604.13883 Joint first-authored work with amazing Frieda Born. 1/5

Researcher Spotlight: Dr. Lukas Muttenthaler BIFOLD PhD graduate Dr. Lukas Muttenthaler is pushing AI beyond raw performance, exploring #representational #alignment. More: t1p.de/pe5z5 @lukasmut.bsky.social @eml-munich.bsky.social @tuberlin.bsky.social @mpicbs.bsky.social @xai-berlin.bsky.social

🚨Go check out our most recent preprint on multilayer attention probing for Vision Transformers! It’s almost as performant as full fine-tuning while being similarly compute efficient as standard linear probing! Plus you get interpretable attention maps! More in 🧵👇🏼 Preprint: arxiv.org/abs/2601.09322

Beyond the final layer: Attentive multilayer fusion for vision transformers

With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a lightweight head, is ...

arxiv.org

Laure Ciernik@lciernik.bsky.social · 7mo ago

Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇

Even though I am not part of the Re-Align organizer team anymore, I am still cheering for this workshop! I still (and will always) believe that Re-Align is a great place for connecting the fields of AI / ML, Neuroscience, and Cognitive Science :) Can highly recommend submitting! 🧠🤖

Badr AlKhamissi@bkhmsi.bsky.social · 7mo ago

🎉 Re-Align is back for its 4th edition at ICLR 2026! 📣 We invite submissions on representational alignment, spanning ML, Neuroscience, CogSci, and related fields. 📝 Tracks: Short (≤5p), Long (≤10p), Challenge (blog) ⏰ Deadline: Feb 5, 2026 for papers 🔗 representational-alignment.github.io/2026/

New post! Last week I shared why I thought cognitive (neuro)science hasn’t contributed as much as one might hope to the design of AI systems; this week I'm sharing my thoughts on how methods and principles from these fields *have* been useful in my work. infinitefaculty.substack.com/p/how-cognit...

How cognitive science can contribute to AI: methods for understanding

#2 in a series on cognitive science and AI

infinitefaculty.substack.com

What aspects of human knowledge do vision models like CLIP fail to capture, and how can we improve them? We suggest models miss key global organization; aligning them makes them more robust. Check out LukasMuttenthaler's work, finally out (in Nature!?) www.nature.com/articles/s41... + our blog! 1/3

Aligning machine and human visual representations across abstraction levels - Nature

Aligning foundation models with human judgments enables them to more accurately approximate human behaviour and uncertainty across various levels of visual abstraction, while additionally improving th...

nature.com

We summarize the ideas in a new DeepMind blog post: deepmind.google/blog/teachin... 2/3

Teaching AI to See the World More Like Humans Do

Aligning AI vision models with human knowledge, improves their robustness and ability to generalize.

deepmind.google

When #AI “thinks” like us - New research out now in @nature.com from @lukasmut.bsky.social & @lampinen.bsky.social & @tuberlin.bsky.social & @mpib-berlin.bsky.social forms a bridge between #human and #machine representation. tinyurl.com/nhd4ycsh

When AI “thinks” like us

New research forms a bridge between human and machine representation

tinyurl.com

🥳 I am incredibly humbled and grateful to share that our work, "Aligning machine and human visual representations across abstraction levels," has been published today in @nature.com ⬇️

Andrew Lampinen@lampinen.bsky.social · 9mo ago

What aspects of human knowledge do vision models like CLIP fail to capture, and how can we improve them? We suggest models miss key global organization; aligning them makes them more robust. Check out LukasMuttenthaler's work, finally out (in Nature!?) www.nature.com/articles/s41... + our blog! 1/3

Pleased to share that our survey "Getting aligned on representational alignment" — on representational alignment across cognitive (neuro)science and machine learning — is now published in TMLR! openreview.net/forum?id=Hiq... Kudos to @sucholutsky.bsky.social @lukasmut.bsky.social for leading this!

Andrew Lampinen@lampinen.bsky.social · 3y ago

What is representational alignment? How can we use it to study or improve intelligent systems? What challenges might we face? In a new paper (arxiv.org/abs/2310.13018 ), we describe a framework that attempts to unify ideas from cognitive science, neuroscience and AI to address these questions. 1/2

Though it’s traumatic for me to bring this up, I was targeted by a group allied with Turning Point USA in 2018, with the help of Tucker Carlson on Fox. My home address circulated online and I got death threats. 1/

I’m recruiting committee members for the Technical Program Committee at #CCN2026. Please apply if you want to help make submission, review & selection of contributed work (Extended Abstracts & Proceedings) more useful for everyone! 🌐 Helps to have: programming/communications/editorial experience.

Grace Lindsay@neurograce.bsky.social · 12mo ago

Fill out the form here to be involved in #CCN2026 tinyurl.com/ccn26committee And definitely fill out the #CCN2025 feedback form! tinyurl.com/ccn25partici...

Join us for the Re-Align^{3} community event & hackathon at #CCN2025 in room A2.11! There will be lots of 🌶️ discussions on 🧠🤖

Interesting reaction from ChatGPT to the HHS mRNA memo. It finds it so implausible that it thinks it's fake. From the perspective of a ~2024(?) trained model, 2025 policies are so absurd as to be unbelievable... chatgpt.com/share/689364...

🎉 Presenting at #ICML2025 tomorrow! Come and explore how representational similarities behave across datasets :) 📅 Thu Jul 17, 11 AM-1:30 PM PDT 📍 East Exhibition Hall A-B #E-2510 Huge thanks to @lorenzlinhardt.bsky.social, Marco Morik, Jonas Dippel, Simon Kornblith, and @lukasmut.bsky.social!

Laure Ciernik@lciernik.bsky.social · last yr.

If two models are more similar to each other than a third on ImageNet, will this hold for medical/satellite images? Our #icml2025 paper analyses how vision model similarities generalize across datasets, the factors that influence them, and their link to downstream task behavior. 🧵1/7

If two models are more similar to each other than a third on ImageNet, will this hold for medical/satellite images? Our #icml2025 paper analyses how vision model similarities generalize across datasets, the factors that influence them, and their link to downstream task behavior. 🧵1/7

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...

Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence

An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...

nature.com

I am deeply grateful to @lorenzlinhardt.bsky.social, Marco Morik, Jonas Dippel, Simon Kornblith, and @lukasmut.bsky.social for their great work and support in this project! We also thank our collaborators, @bifold.berlin and HFA 7/7 📄Paper: arxiv.org/abs/2411.05561 💻Code: github.com/lciernik/sim...

Objective drives the consistency of representational similarity across datasets

The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irrespective of the obje...

arxiv.org

🚨Have a look at our most recent work on the driving factors of representational similarities! This work will be presented at ICML 2025! See thread below 🧵

Laure Ciernik@lciernik.bsky.social · last yr.

If two models are more similar to each other than a third on ImageNet, will this hold for medical/satellite images? Our #icml2025 paper analyses how vision model similarities generalize across datasets, the factors that influence them, and their link to downstream task behavior. 🧵1/7

Please everyone gather and go outside, protest, march, do something! We cannot let science and academia be destroyed by the worst administration the US has ever had.

The New York Times@nytimes.com · last yr.

Breaking News: The Trump administration halted Harvard’s ability to enroll international students, a major escalation in its battle with the school.

How do language models generalize from information they learn in-context vs. via finetuning? In arxiv.org/abs/2505.00661 we show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. 1/

That’s a wrap for Re²-Align 🤖 🧠👤 at #ICLR2025 🦁🥳— thanks for being part of it! We’re probably just getting started… see you all at Re³-Align🌷😉

Siddharth Suresh@siddsuresh97.bsky.social · last yr.

✨ Come join us at the Second edition of the Re-Align workshop @iclr_conf ! 🚀🧠 The workshop explores the fascinating question of how artificial and biological systems align in their representations of the world. #ReAlign #ICLR2025

✨ Come join us at the Second edition of the Re-Align workshop @iclr_conf ! 🚀🧠 The workshop explores the fascinating question of how artificial and biological systems align in their representations of the world. #ReAlign #ICLR2025

Harvard has set an example for other higher-ed institutions - rejecting an unlawful and ham-handed attempt to stifle academic freedom, while taking steps to make sure students can benefit from an environment of intellectual inquiry, rigorous debate and mutual respect. Let’s hope others follow suit.

Here’s an extended version of my PhD defense talk about our work on *representational alignment between humans 🧠 and machines 🤖 for computer vision* that I gave two weeks ago at the Max Planck Institute for Human Cognitive and Brain Sciences www.cbs.mpg.de/cbs-coconut/...

video-muttenthaler

Lukas Muttenthaler (Google DeepMind, TU Berlin)

cbs.mpg.de

🚨 We're hiring! If you're excited about 🤖 ML/LLMs, 🧠 cognitive science, or 💭 computational psychiatry, come join us in Munich. Two fully funded PhDs @www.helmholtz-munich.de: tailored mentorship, international vibe, lots of room to grow. 📅 May 16th 🔗 hcai-munich.com/PhDHCAI.pdf

This past Friday I successfully defended my PhD 🎉 🎊👨🏻‍🎓 What a journey it was! 4.5 years of many ups and many downs. Can’t believe it’s over. I am still processing… Special thanks to my wonderful committee KR Müller, @martinhebart.bsky.social, @ericschulz.bsky.social, and @scychan.bsky.social!

Thank you for having me!

CBS CoCoNUT@cbs-coconut.bsky.social · last yr.

Join us for the talk by @lukasmut.bsky.social (Google Deep Mind, TU Berlin) on "Representational alignment of humans and machines for computer vision" Starting soon at 10 AM (CET), March 28 Find the Zoom Link here: www.cbs.mpg.de/en/cbs-coconut