@asmith4981@ajassy Agreed. It also skips why the astrophage all died during the return trip, so the centrifuge solution feels like it comes out of nowhere.
@icmlconf Are reserved spots also available for workshop-paper authors or volunteers? In particular for workshop authors, travelling to Seoul for only two days is difficult if registration is already full.
From these graphs, we derive zero-shot VLM tasks, including phrase grounding, HOI recognition, referring, activity recognition, relation extraction, and temporal VQA. You can also sample more instances around hard tasks and reuse the labeling + graph recipe for your own dataset.
Check out our new preprint, dataset, and benchmark BARISTA if you are a coffee lover like we are and interested in VLM benchmarking and debugging temporal & spatial reasoning in egocentric video.
Preprint: https://t.co/rp9fhGKQ0j
Dataset: https://t.co/oGxUfcPoc4
BARISTA provides densely annotated coffee-preparation videos with per-frame scene graphs, object identities, masks, boxes, relations, HOIs, activities, and process steps.
Thanks to all great collaborators from @ramblr.ai
@AmitLeViAI@icmlconf I feel you. We’ve also had strong scores several times, but then got rejected by the AC because one reviewer pushed against interpretability (performance tradeoff) and the AC followed that line.
We structure the design space of CBMs, disentangle key components, discuss recurring challenges, and outline a roadmap for how the field could evolve.
If we missed any relevant work published up to the end of 2025, please let us know—we will incorporate it in the next version.
What’s in the Bottle? A Survey and Roadmap of Concept Bottleneck Models
CBMs are a rapidly growing direction in interpretable-by-design machine learning. However, the field has become increasingly fragmented.
Preprint Link:
https://t.co/SHspTEW8P2
Together with David Steinmann , Udo Schlegel, and @WolfStammer , we decided it was time to take a step back: we review and categorize more than 100 papers into a unified architectural taxonomy. In doing so, we try to answer the question: What’s in the bottle?
We wish everyone the best for tomorrow’s decisions—and try not to take them too seriously. 🙂
If you’re curious what tabular ML predicts for your paper, you can check it out here:
Website: https://t.co/xzS97oGNcB
Code: https://t.co/KleJBcpVUr
We took a quick look at the new PaperDecision results for ICLR 2026 and asked a simple question: do we really need LLMs for this task?
We did the same but with tabular data:
https://t.co/rx1P8BU03G
#ICLR2026
Framing the problem as primarily tabular—using numerical review scores such as soundness and contribution—we evaluated standard tabular models (TabPFN, CatBoost, logistic regression, and decision trees) without any hyperparameter tuning.
@bfl_ml Congrats! Also a huge milestone for AI in south Germany :)
Just out of curiosity ( I haven’t been able to find sth on the website), are you also offering PhD internships?
@rdesh26@iclr_conf Sure, totally agree. We'll prepare a brief summary for the AC to summarize the work of our rebuttal, since we had some useful discussions.