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Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness
The preprint tests whether legal models' named statutes or precedents causally support their verdicts. Holding case facts fixed, it substitutes the named authority and examines verdict changes and hidden-state dynamics…
The AI Assessment Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
The preprint describes an open-source framework for technical testing in AI regulatory sandboxes, motivated by EU AI Act obligations. It proposes a plug-in test and control catalogue, a shared data model for…
Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation
Researchers have developed a system that routes AI queries to the most effective and efficient model based on costly value estimation. The system, called Pandora's Router, uses a trade-off between fast but noisy…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered architecture for hospital AI systems aimed at addressing the challenges of isolated AI deployments in hospitals. The proposed architecture includes an Agent Orchestration Layer, a…
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
The research proposes a multi-layered compliance-first Agentic AI architecture for hospital systems. The architecture aims to address the challenges of siloed AI deployments in hospitals by integrating various layers…
Regulating autonomous and agentic AI
This research paper discusses the difficulties in regulating activities that use autonomous and agentic AI due to changes in traditional regulatory assumptions about knowledge and control. The authors analyze four…
Explaining Reinforcement Learning Agents via Inductive Logic Programming
This research aims to improve Reinforcement Learning (RL) policies by making them more transparent and interpretable. It proposes objective and planning-oriented metrics for policy explainability in RL settings using…
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