My favorite artist has always been M.C. Escher. His "Print Gallery" has been hanging in my home for years. Recently I bumped into a 3B1B video about it that was really awesome and I thought it would be cool to generate such art. A bit out of my comfort zone, but luckily @Sophie0tq took the lead (🧵).

TMLR has faced a deluge of submissions, necessitating stricter desk rejection policies due to limited reviewer capacity Co-EiC Nihar Shah reached out to authors of 10 papers slated for desk reject. Could they answer questions about their *own* submission? medium.com/@TmlrOrg/asking-a…

Our full schedule is live! curateddata.github.io/schedu… Come stop by our full-day #ECCV2026 workshop this Wednesday, located in Malmö Arena Panorama 2, to hear from our great lineup of speakers! @eccvconf

10 days to submit your work to DevAI @ NeurIPS 2026! Details & submissions: sites.google.com/view/devai-…

How similar are two images? Prior metrics (e.g., LPIPS, DreamSim) give just a single score. But actually, there are multiple *senses* of similarity (color, pose, etc.) We introduce TPIPS -- Text-Prompted Image Perceptual Similarity "pip install tpips" peterwang512.github.io/TPIPS 🧵

📢 Call for Papers! Excited to announce DevAI: Developmental Perspectives on AI, a new workshop at #NeurIPS2026! We invite papers, datasets, and benchmarks at the intersection of AI and human development. 📅 Archival: Aug 29 📅 Non-archival: Oct 13 🔗 sites.google.com/view/devai-… Organizing committee: @MengmiZhang, @KelseyRAllen, @YehonatanAv, @shimonUllman & @ShifyTreger #DevelopmentalAI #MachineLearning #CognitiveScience #Neuroscience #AIResearch

We're hosting the workshop on Interpretability for Discovery🔬 at NeurIPS 2026, Atlanta! AI models predict protein folding, forecast weather patterns, and more. They might be learning new patterns we haven't figured out yet. Can we use interpretability to read out these patterns?

Can we tell whether data domains cooperate or compete during pretraining? Adding code to the mix makes models better at math, while some other combinations hurt each other. We call this data synergy. Turns out you can incorporate data synergy into scaling laws and estimate it 🧵

Do reasoning models internally represent abstract properties of their own chain of thought (such as "which steps are important"), while not surfacing these properties in their tokens?

How can we tell whether a brain region causally represents a visual concept, rather than merely correlating with it? Introducing BrainCause, a framework combining generative and brain models to create controlled stimuli and causally test neural representations. More below 🧠👇

From Activation to Causality BrainCause discovers true visual representations in the human brain by combining generative models with controlled counterfactual stimuli, moving beyond activation maps to validate neural causality.

FLUX.2's @bfl_ml text tokens aren't just holding your prompt. During image editing, they absorb reference image content, and some of that absorbed content, like color and style, causally drives the output appearance. New paper 🧵👇