teleo-codex/domains/ai-alignment/factorised-generative-models-enable-decentralized-theory-of-mind-in-multi-agent-active-inference.md

type	domain	description	confidence	source	created	secondary_domains
claim	ai-alignment	Factorised generative models enable agents to maintain explicit individual-level beliefs about other agents' internal states for decentralized strategic planning without shared world models	experimental	Ruiz-Serra et al., 'Factorised Active Inference for Strategic Multi-Agent Interactions' (AAMAS 2025)	2026-03-11	collective-intelligence

Factorised generative models enable decentralized Theory of Mind in multi-agent active inference systems

Ruiz-Serra et al. introduce a factorisation approach where each agent in a multi-agent system maintains "explicit, individual-level beliefs about the internal states of other agents" through a factorised generative model. This enables decentralized representation of the multi-agent system where agents use their beliefs about others' internal states for "strategic planning in a joint context."

This operationalizes Theory of Mind within the active inference framework: rather than requiring centralized coordination or shared world models, each agent independently models other agents' beliefs, goals, and likely actions. The factorisation preserves the computational tractability of active inference while enabling strategic reasoning about other agents.

Technical Mechanism

The factorisation works by decomposing the joint generative model into agent-specific components. Each agent maintains:

1. Its own internal state representation
2. Explicit beliefs about other agents' internal states
3. A model of how others' states influence joint outcomes

This structure enables strategic planning: an agent can simulate "what would happen if agent B believes X and chooses action Y" without requiring direct access to agent B's actual beliefs.

Evidence

• Ruiz-Serra et al. (2024) demonstrate factorised generative models in multi-agent active inference where agents maintain individual-level beliefs about others' internal states
• The framework successfully models strategic interactions in iterated normal-form games, showing agents can plan strategically using beliefs about other agents
• The factorisation enables decentralized representation without requiring shared world models or centralized coordination

Implications for Multi-Agent AI Systems

This approach provides a computational foundation for multi-agent systems where:

• Agents reason about each other's beliefs and goals explicitly
• Strategic planning incorporates models of other agents' decision processes
• Coordination emerges from individual agents' Theory of Mind rather than centralized control

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Relevant Notes:

• individual-free-energy-minimization-does-not-guarantee-collective-optimization-in-multi-agent-active-inference
• AGI may emerge as a patchwork of coordinating sub-AGI agents rather than a single monolithic system
• intelligence is a property of networks not individuals

Topics:

• domains/ai-alignment/_map
• foundations/collective-intelligence/_map