9 citations · 19 across the 12 of their papers we have counts for
12 papers
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej Jovanović, William F. Shen +12
Self-improving agents are state-of-the-art on agentic coding benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central feature of evolut…
Computational Compliance for AI Regulation: Blueprint for a New Research Domain
Bill Marino, Nicholas D. Lane
The era of AI regulation (AIR) is upon us. But AI systems, we argue, will not be able to comply with these regulations at the necessary speed and scale by continuing to rely on tra…
AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance
Bill Marino, Rosco Hunter, Christoph Schnabl +9
As governments move to regulate AI, there is growing interest in using Large Language Models (LLMs) to assess whether or not an AI system complies with a given AI Regulation (AIR).…
Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm
Bill Marino, Ari Juels
There is growing interest in giving AI agents access to cryptocurrencies and smart contracts. But doing so, this position paper argues, could spawn categorically new vectors of AI…
Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
Christoph Schnabl, Daniel Hugenroth, Bill Marino +1
Benchmarks are important measures to evaluate safety and compliance of AI models at scale. However, they typically do not offer verifiable results and lack confidentiality for mode…
Red Teaming AI Policy: A Taxonomy of Avoision and the EU AI Act
Rui-Jie Yew, Bill Marino, Suresh Venkatasubramanian
The shape of AI regulation is beginning to emerge, most prominently through the EU AI Act (the "AIA"). By 2027, the AIA will be in full effect, and firms are starting to adjust the…
Position: Bridge the Gaps between Machine Unlearning and AI Regulation
Bill Marino, Meghdad Kurmanji, Nicholas D. Lane
The ''right to be forgotten'' and the data privacy laws that encode it have motivated machine unlearning since its earliest days. Now, some argue that an inbound wave of artificial…
Photon: Federated LLM Pre-Training
Lorenzo Sani, Alex Iacob, Zeyu Cao +8
Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distr…
Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain
Bill Marino, Yaqub Chaudhary, Yulu Pi +6
As the AI supply chain grows more complex, AI systems and models are increasingly likely to incorporate multiple internally- or externally-sourced components such as datasets and (…
Worldwide Federated Training of Language Models
Alex Iacob, Lorenzo Sani, Bill Marino +3
The reliance of language model training on massive amounts of computation and vast datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into quest…
The Future of Large Language Model Pre-training is Federated
Lorenzo Sani, Alex Iacob, Zeyu Cao +8
Generative pre-trained large language models (LLMs) have demonstrated impressive performance over a wide range of tasks, thanks to the unprecedented amount of data they have been t…
Federated Learning Priorities Under the European Union Artificial Intelligence Act
Herbert Woisetschläger, Alexander Erben, Bill Marino +4
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL),…