Good morning #ICLR2026 ☀️
The Re-Align workshop kicks off in under an hour! A full day on what we can actually do with representational alignment, from brains to language models to agents.
Schedule in the thread 👇
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! 🧠🤖
🚨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
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
🚨 Hiring in Munich 🇩🇪: 2 open-topic PhD positions in human & machine learning (TVöD E13 80%).
Start ~June 2026 (flexible). Deadline: March 2, 2026.
Apply/info: hcai-munich.com/PhD_Job_Ad.pdf
Reposts appreciated 🙏
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...
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
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
We summarize the ideas in a new DeepMind blog post: deepmind.google/blog/teachin... 2/3
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
New research forms a bridge between human and machine representation
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...
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 ...
🎉 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/
BIFOLD Berlin Institute for the Foundations of Learning and Data
Badr AlKhamissi
Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇
