swarms

This paper proposes a hierarchical hybrid LLM-MARL architecture for agentic AI networking in low-altitude wireless networks, enabling coordinated coexistence of heterogeneous unmanned aerial systems by adapting to changing service requirements without retraining.

This research explores the use of self-play reinforcement learning to enable drone swarms to autonomously develop both melee and ranged battle tactics without human intervention.

Neural_avb highlights how Minimax M3's RLMs use subagent swarms with pydantic contracts for type checking and schema validation, reducing hallucination rates and failed subagent calls.