Derives exact closed-form increment to the order parameter from each adversarial kick in Kuramoto networks, independent of coupling strength, using Ott-Antonsen reduction.
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ScioMind combines anchoring-based belief updates, hierarchical memory, and dynamic profiles in LLM multi-agent systems to produce more stable, diverse, and psychologically aligned opinion trajectories than prior fixed-rule or unconstrained approaches.
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Analytical foundation for adversarial synchronization control in oscillator networks
Derives exact closed-form increment to the order parameter from each adversarial kick in Kuramoto networks, independent of coupling strength, using Ott-Antonsen reduction.
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ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles
ScioMind combines anchoring-based belief updates, hierarchical memory, and dynamic profiles in LLM multi-agent systems to produce more stable, diverse, and psychologically aligned opinion trajectories than prior fixed-rule or unconstrained approaches.