Michelle Bakels (@MichelleBakels)
Day 21: Google Labs의 Kath Korevec가 'Proactive Agents'를 발표했습니다. 또한 'State of MechInterp' 세션에서는 SAEs의 생산 적용, 회로 추적(circuit tracing), AI4Science 적용 사례와 더 실용적인 해석('Pragmatic' Interp) 접근법 등 기계 해석학(mechanistic interpretability) 관련 최신 연구·토픽을 다뤘습니다.
Is it AI "Hallucination" or "Intuition"? 🧠
Great discussions today on the Prime-Chaos discovery. The skepticism is valid: Python scripts prove the stats, but not the truth.
So, we are leveling up OpenSciEval.
🚀 Phase 2 Mission: Tasking the Agent to translate its heuristic derivation into Lean 4 code for formal verification.
If it compiles, the debate ends. Stay tuned.
Also consider following the authors Aniket Pramanick (Ubiquitous Knowledge Processing (UKP) Lab), Yufang Hou (IT:U- Interdisciplinary Transformation University Austria, IBM Research), Saif M Mohammad (National Research Council Canada / Conseil national de recherches Canada), and Iryna Gurevych (Ubiquitous Knowledge Processing (UKP) Lab).
🗺️ See you at #ACL2025 in Vienna
(2/2)
How successful has the SciML Small Grants program been at getting newcomers to contribute to open source software #oss for #sciml #julialang and #ai4science? Very!
* 13 total projects initiated
* ~90% success rate
See the blog post summary of the first year!
https://sciml.ai/news/2025/07/20/sciml_small_grants_year_one_success/
Our new manuscript shows how to extend automated model discovery and universal differential equations to chaotic systems in #neuroscience using a trick from control literature known as the Prediction Error Method (PEM)!
(copied from post on X by Jure Leskovec)
Announcing Biomni — the first general-purpose biomedical AI agent. Biomni is a free web platform where biomedical scientists can immediately delegate their tasks to Biomni.
Biomni is an open-source initiative: we invite the community to build on it and advance biomedical research at scale.
- Try it now: https://biomni.stanford.edu
- Paper: https://biomni.stanford.edu/paper.pdf
- Code: https://github.com/snap-stanford/biomni
New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.
#sciml #ai4science #hardconstraints #neuralnetworks #dae #acausal #modelingtoolkit #julialang #modelica
We recently released a new manuscript Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints where we showed a way to develop neural networks where any arbitrary constraint function can be directly imposed throughout the evolution equation to near floating point accuracy. However, in true academic form it focuses directly on getting to the point about the architecture, but here I want to elaborate about the mathematical structures that surround the object, particularly the differential-algebraic equation (DAE), how its various formulations lead to the various architectures (such as stabilized neural ODEs), and elaborate on the other related architectures which haven’t had a paper yet but how you’d do it (and in what circumstances they would make sense).
We're #recruiting a PhD student in machine learning at Sorbonne University (Paris) for Fall 2025!
Topic: AI4Science & Generative Models
Title: Deep Generative Models of Physical Dynamics
See more details here:
https://pages.isir.upmc.fr/gallinari/open-positions/
sayzard
Jean-Philip Piquemal
mariswang
UKP Lab
Dr. Chris Rackauckas 
Space Data Scientist
MottG
MLIA ISIR