arXiv subjects

Raymond Koopmanschap

Publications and source records attributed to Raymond Koopmanschap.

4 recordsLinked to original sources

arXiv2026

How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements

Several international agreements have been proposed to regulate frontier AI development in response to catastrophic risks. However, there is no structured way to evaluate whether these proposals are enforceable, to assess where they might fail in practice, or to determine which combination of policies is most effective. We propose a taxonomy based on the principle that wherever sufficient capacity exists to violate an agreement, it must be under a control regime. This decomposes the problem of ensuring compliance with the agreement into preventing uncontrolled resource acquisition, detecting all capacity outside the control regime, and preventing escape from the control regime. Existing proposals consist of individual policies that address one or more of these sub-problems. Because the compute required for dangerous capabilities may decrease over time, more actors can violate an agreement and enforcement of these policies becomes harder. We define the enforcement breaking point as the FLOP-threshold or equivalent metric at which a policy loses its effectiveness in solving the sub-problem, and introduce a set of factors to assess how and why this breakdown occurs. This reveals which of the three sub-problems any given proposal adequately addresses, and where enforceability breaks down first. Applying this taxonomy to existing proposals reveals that more focus is placed on preventing escape from the control regime, while preventing resource acquisition and detecting all capacity outside the control regime receive less attention. By making these gaps explicit, this taxonomy can help researchers and policymakers prioritize future enforcement and verification efforts.

cs.CY↗

arXiv2025

Toward a Global Regime for Compute Governance: Building the Pause Button

As AI capabilities rapidly advance, the risk of catastrophic harm from large-scale training runs is growing. Yet the compute infrastructure that enables such development remains largely unregulated. This paper proposes a concrete framework for a global "Compute Pause Button": a governance system designed to prevent dangerously powerful AI systems from being trained by restricting access to computational resources. We identify three key intervention points -- technical, traceability, and regulatory -- and organize them within a Governance--Enforcement--Verification (GEV) framework to ensure rules are clear, violations are detectable, and compliance is independently verifiable. Technical mechanisms include tamper-proof FLOP caps, model locking, and offline licensing. Traceability tools track chips, components, and users across the compute supply chain. Regulatory mechanisms establish constraints through export controls, production caps, and licensing schemes. Unlike post-deployment oversight, this approach targets the material foundations of advanced AI development. Drawing from analogues ranging from nuclear non-proliferation to pandemic-era vaccine coordination, we demonstrate how compute can serve as a practical lever for global cooperation. While technical and political challenges remain, we argue that credible mechanisms already exist, and that the time to build this architecture is now, before the window for effective intervention closes.

cs.CY↗

arXiv2024

Inducing Human-like Biases in Moral Reasoning Language Models

In this work, we study the alignment (BrainScore) of large language models (LLMs) fine-tuned for moral reasoning on behavioral data and/or brain data of humans performing the same task. We also explore if fine-tuning several LLMs on the fMRI data of humans performing moral reasoning can improve the BrainScore. We fine-tune several LLMs (BERT, RoBERTa, DeBERTa) on moral reasoning behavioral data from the ETHICS benchmark [Hendrycks et al., 2020], on the moral reasoning fMRI data from Koster-Hale et al. [2013], or on both. We study both the accuracy on the ETHICS benchmark and the BrainScores between model activations and fMRI data. While larger models generally performed better on both metrics, BrainScores did not significantly improve after fine-tuning.

cs.AI↗

arXiv2019

Learning to Request Guidance in Emergent Communication

Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent. The guide achieves this by providing the agent with discrete messages in an emerged language about how to solve the task. We extend this one-directional communication by a one-bit communication channel from the learner back to the guide: It is able to ask the guide for help, and we limit the guidance by penalizing the learner for these requests. During training, the agent learns to control this gate based on its current observation. We find that the amount of requested guidance decreases over time and guidance is requested in situations of high uncertainty. We investigate the agent's performance in cases of open and closed gates and discuss potential motives for the observed gating behavior.

cs.AI↗