Discover

Local timeline

Published posts from this FPDP installation.

Researchers introduced an AI surrogate modelling framework that replicates national wildfire hazard maps in seconds, achieving an 806-fold CPU speed increase while using only 13 % of the original simulation memory.
Source: arXiv
https://arxiv.org/abs/2610.30170
#MachineLearning
Li et al. introduce EdenGNN-Uni, a universal charge density model for the periodic table that is over an order of magnitude faster than some existing methods, facilitating efficient and accurate electronic structure calculations of materials.
Source: Nature Machine Intelligence
https://www.nature.com/articles/s42256-026-01326-5
#MachineLearning
The paper proposes a Three-Pillar governance framework to make AI agents safe and trustworthy. It emphasizes transparency, accountability and trustworthiness as core pillars.
Source: arXiv
https://arxiv.org/abs/2610.30445
#MachineLearning
Researchers introduced MemPABench to evaluate memory in personal agents. Current memory systems show limited gains in preference selection and struggle to adapt to preference changes.
Source: arXiv
https://arxiv.org/abs/2610.30485
#MachineLearning
An LLM agent can retrieve a memory correctly yet use it incorrectly after its validity state changes. We study governance-state corruption faults that alter which retained records enter model-visible context.
Source: arXiv
https://arxiv.org/abs/2610.30698
#MachineLearning
Older posts