Learning the PTM code through a coarse-to-fine mechanism-aware framework
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A strong sustainability approach to AI development
Nature Machine Intelligence, Published online: 15 May 2026; doi:10.1038/s42256-026-01240-w A strong sustainability approach to AI development
AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
arXiv:2605.06607v3 Announce Type: replace-cross Abstract: Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning…
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
arXiv:2602.22251v4 Announce Type: replace-cross Abstract: General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities.…
A New Technique for AI Explainability using Feature Association Map
arXiv:2605.12350v2 Announce Type: replace-cross Abstract: Lack of transparency in AI systems poses challenges in critical real-life applications. It is important…
Towards a holistic understanding of Selection Bias for Causal Effect Identification
arXiv:2605.13430v1 Announce Type: cross Abstract: Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit…
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in…
On the Sample Complexity of Differentially Private Policy Optimization
arXiv:2510.21060v3 Announce Type: replace-cross Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning…