1 citations · 1 across the 2 of their papers we have counts for
9 papers
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej JovanoviÄ, William F. Shen +10
Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods generally assume a…
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…
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 as well as to the smart contracts that transact them. But doing so, this position paper argues, could lead…
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…
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…