Antonia Wüst
@toniwuest
PhD student at AI/ML Lab @TUDarmstadt Interested in concept learning, neuro-symbolic AI and program synthesis
I'll be at #ICML2025 next week presenting our recent work on VLMs and Bongard Problems! Feel free to reach out, happy to have a chat ☺️
📢 New LLM benchmark out, built to test logical reasoning! 🚂🧩 Evaluate your LLM on our SLR-Bench or create your own benchmark with our SLR framework 🚀 Check it out 👉 huggingface.co/datasets/AIML-…
Want to enhance the reasoning skills of today’s LLMs? 🚀 Check out SLR, our latest framework on Scalable Logical Reasoning. 🧠 Systematically train & evaluate LLMs on challenging, customizable reasoning tasks with RL & SFT. 🔗 Paper & dataset below
Had a fantastic time at the Women in Data Science (WiDS) Zurich conference today! I had the chance to present my work on Bongard Problems and connect with many inspiring women in data science. Grateful for the insightful talks and engaging conversations! ✨ #WiDS2025 #WomenInTech
🔥"Where is the Truth? The Risk of Getting Confounded in a Continual World" got a spotlight poster @icmlconf ! arxiv.org/abs/2402.06434 -> we introduce continual confounding + the ConCon dataset, where confounders over time render continual knowledge accumulation insufficient⬇️
🧠Foundation models are powerful—but what happens when they remember too much? Join us at #ICML2025 for our workshop on “The Impact of Memorization on Trustworthy Foundation Models” 👉icml2025memfm.github.io Let’s talk about memorization & what it takes to build trustworthy AI!
Happy to present our work now, come by and say hi! ☺️
Excited to be at #NeurIPS2024 this week presenting our work Neural Concept Binder! 🤗 Stop by our poster to see how we derive expressive concept representations from unlabeled images. ⏰ Thu, Dec 12 11am–2pm 📍 East Hall A-C, #2103 See you there! 🎉✨
Curious about the differences between shortcuts, spurious correlations, and confounders? 🤔 Our new paper explains similarities and differences and introduces a comprehensive taxonomy of shortcut learning, reviewing the state of the field. Check it out! arxiv.org/abs/2412.05152
So happy to share that our paper V-LoL: A Diagnostic Dataset for Visual Logical Learning has been accepted @DMLRJournal🎉If you're looking for novel visual datasets designed to evaluate the logical learning capabilities of modern AI systems, check it out! arxiv.org/abs/2306.07743
The first “ConCon” challenge, tackling continual confounding, has just been launched!💡 Submit 3-page reports (non-archival) as part of the third Cont. Causal. Bridge at AAAI@2025. (Early deadline is Dec 6, 2024 AoE) Check out the full announcement here: continualcausality.org/challenge/
Aktuelle #KI-Modelle bestehen nicht KI-Benchmarks aus den 1960iger Jahren 😤 Tolle Zusammenarbeit mit @toniwuest @philosotim @lukas_helff @devendratweetin @c_rothkopf heise.de/news/Vision-La…