AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance

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). However, there is presently no way to benchmark the performance of LLMs at this task. To fill this void, we introduce AIReg-Bench: the first benchmark dataset designed to test how well LLMs can assess compliance with the EU AI Act (AIA). We created this dataset through a two-step process: (1) by prompting an LLM with carefully structured instructions, we generated 120 technical documentation excerpts (samples), each depicting a fictional, albeit plausible, AI system - of the kind an AI provider might produce to demonstrate their compliance with AIR; (2) legal experts then reviewed and annotated each sample to indicate whether, and in what way, the AI system described therein violates specific Articles of the AIA. The resulting dataset, together with our evaluation of whether frontier LLMs can reproduce the experts' compliance labels, provides a starting point to understand the opportunities and limitations of LLM-based AIR compliance assessment tools and establishes a benchmark against which subsequent LLMs can be compared. The dataset and evaluation code are available at https://github.com/camlsys/aireg-bench.

The AI Agent Index

Leading AI developers and startups are increasingly deploying agentic AI systems that can plan and execute complex tasks with limited human involvement. However, there is currently no structured framework for documenting the technical components, intended uses, and safety features of agentic systems. To fill this gap, we introduce the AI Agent Index, the first public database to document information about currently deployed agentic AI systems. For each system that meets the criteria for inclusion in the index, we document the system's components (e.g., base model, reasoning implementation, tool use), application domains (e.g., computer use, software engineering), and risk management practices (e.g., evaluation results, guardrails), based on publicly available information and correspondence with developers. We find that while developers generally provide ample information regarding the capabilities and applications of agentic systems, they currently provide limited information regarding safety and risk management practices. The AI Agent Index is available online at https://aiagentindex.mit.edu/

Fast Inference Through The Reuse Of Attention Maps In Diffusion Models

Text-to-image diffusion models have demonstrated unprecedented abilities at flexible and realistic image synthesis. However, the iterative process required to produce a single image is costly and incurs a high latency, prompting researchers to further investigate its efficiency. Typically, improvements in latency have been achieved in two ways: (1) training smaller models through knowledge distillation (KD); and (2) adopting techniques from ODE-theory to facilitate larger step sizes. In contrast, we propose a training-free approach that does not alter the step-size of the sampler. Specifically, we find the repeated calculation of attention maps to be both costly and redundant; therefore, we propose a structured reuse of attention maps during sampling. Our initial reuse policy is motivated by rudimentary ODE-theory, which suggests that reuse is most suitable late in the sampling procedure. After noting a number of limitations in this theoretical approach, we empirically search for a better policy. Unlike methods that rely on KD, our reuse policies can easily be adapted to a variety of setups in a plug-and-play manner. Furthermore, when applied to Stable Diffusion-1.5, our reuse policies reduce latency with minimal repercussions on sample quality.