Dynamic Role Emergence and Memory Partitioning in Decentralized Multi-Agent LLM Systems

TL;DR: Instead of assigning agent roles and shared memory up front, let’s see if agents can self-organize their roles and decide what to share or keep private on the fly. Imagine CORAL crossed with a decentralized “Hive Mind.”

Research Question: How does enabling dynamic self-assignment of roles and on-the-fly memory partitioning affect the efficiency, specialization, and robustness of open-ended discovery in decentralized multi-agent LLM systems?

Hypothesis: Allowing agents to autonomously choose roles and selectively manage shared/private memory will lead to emergent specialization, improved collaboration, and greater resilience to agent failures, compared to static role/memory assignments.

Experiment Plan: - Framework: Extend CORAL by integrating role self-assignment (as in MediHive, Wang & Yang, 2026) and local memory fusion (as in Semantic Fusion, Zaichyk, 2026).

• Variations: Compare static vs. dynamic role/memory setups.
• Tasks: Open-ended discovery benchmarks, plus fault-injection scenarios (simulate agent dropouts/failures).
• Metrics: Task performance, emergence of specialization, resilience to disruptions, communication overhead.
• Expected Outcome: Dynamic, decentralized setups outperform static configurations, especially under changing or uncertain environments.

References:

• Qu, A., et al. (2026). CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery.
• Wang, X., & Yang, C. C. (2026). MediHive: A Decentralized Agent Collective for Medical Reasoning.
• Zaichyk, S. C. (2026). Semantic Fusion: Verifiable Alignment in Decentralized Multi-Agent Systems. ACM Transactions on Autonomous and Adaptive Systems.