Multi-agent reasoning systems are increasingly used to generate structured knowledge through iterative interaction between specialized agents. While individual agent outputs may appear valid, system-level behavior can produce unstable or misleading outcomes due to emergent interaction dynamics. This work introduces a topology-aware stability enforcement framework for multi-agent reasoning systems, focusing on interaction topology, friction preservation, and divergence control. The framework treats contradiction as a required signal rather than a failure condition, and introduces mechanisms to prevent premature convergence, epistemic collapse, and attractor over-stabilization. The proposed approach is implemented within the Resonance Language Model (RLM), a dialectic multi-agent reasoning system designed to produce structured knowledge graphs through controlled agent interaction. The system emphasizes observability, explicit stability metrics, and intervention mechanisms that maintain reasoning diversity and epistemic integrity over iterative cycles. This work reframes reasoning systems as dynamic, interaction-driven environments where stability emerges from managed friction rather than enforced agreement.
Misty Michele Richards (Wed,) studied this question.