The following topics are not in scope, either because they are being pursued through other channels, because they concern individual rather than multi-principal, multi-agent safety problems, or because they fall outside the technical, system-level focus of the call:

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• Single-agent safety. Alignment, interpretability, robustness, and oversight of individual AI agents, including defences against environmental attacks such as prompt injection, jailbreaks, and malicious web content, and methods for their detection.
• Capability advancement. Work aimed primarily at increasing the individual or collective capabilities of AI agents – including their general cooperative capabilities – in the absence of a clear safety motivation. If the primary contribution is a system that performs better at a task, and the safety analysis is secondary or post hoc, the work is out of scope regardless of framing.
• Individual-agent cooperation without system-level evaluation. Training methods, model specifications, or constitutions for making individual agents more cooperative, where evaluation stops at the individual or pairwise level.
• Solutions relying on a single agent deployer (‘principal’). Certain solutions to multi-agent problems rely on the existence of a privileged overseer or principal who governs and controls all agents. These solutions do not apply to the multi-principal deployments we focus on in this call.
• AI for human cooperation. AI-facilitated negotiation and mediation, AI for democratic institutions, and other applications where the primary objective is helping humans cooperate with one another rather than addressing the safety of multi-agent AI systems.
• Agentic inequality and power concentration. Although related to multi-agent safety, technical interventions that target disparities in who can deploy agents with different capabilities constitute a distinct agenda and are out of scope here.
• Non-technical work. Purely conceptual, philosophical, or policy-oriented contributions that do not involve technical elements or a plausible path to real-world technical implementation.
• Toy systems. Proposals that do not engage with frontier-model agents under realistic deployment conditions. This includes classical game-theoretic settings (e.g., iterated normal-form games or simple auctions with discrete action spaces) without a concrete and credible methodology for extending results to populations of natural-language (or multimodal) agents that use tools and have persistent memory in realistic environments.
• Naive application of pre-existing solutions. Proposals that straightforwardly apply standard mechanisms for identity, reputation, commitment, or trust (e.g., blockchain-based identity, human reputation systems) to AI agents without addressing challenges specific to the AI-agent setting - such as agent clonability, the absence of persistent biological identity, or machine-speed interactions.
• Commercial product development.  This is a philanthropic funding call targeting work that markets would not otherwise do, and proposals will be evaluated through that lens. That said, we welcome research that produces open tools and frameworks, as well as foundational work that may yield downstream applications beyond the project’s immediate philanthropic purposes.