arXiv AI

Comprehensive AI governance requires addressing non-model gains

arXiv:2606. 00047v1 Announce Type: cross Abstract: Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function of the compute and data used during training.

arXiv Machine Learning
Aug 7

A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance

arXiv:2608. 06246v1 Announce Type: new Abstract: Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-efficient adaptation, alignment, retrieval augmentation, model editing, unlearning, calibration, and Multimodal Instruction Tuning.

By Fardin Afdideh, Fernando Seoane, Farhad Abtahi
arXiv AI
6d ago

Governing Agentic AI in FinTech

arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.

By Henry Han
arXiv AI
Jul 1

RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies

arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.

By Patricia Paskov, Kevin Wei, Shen Zhou Hong, Dan Bateyko, Xavier Roberts-Gaal, Carson Ezell, Gailius Praninskas, Valerie Chen, Umang Bhatt, Ella Guest