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Talk is Flight: How RALLY Bridges Language and Learning in UAV Swarms

When language models take flight, consensus becomes not just possible, but programmable. Modern UAV swarms face the daunting task of coordinating across partial observability, adversarial threats, and shifting missions. Traditional Multi-Agent Reinforcement Learning (MARL) offers adaptability, but falters when role differentiation or semantic reasoning is required. Large Language Models (LLMs), meanwhile, understand tasks and intent—but lack grounded, online learning. RALLY (Role-Adaptive LLM-Driven Yoked Navigation) is the first framework to successfully integrate these two paradigms, enabling real-time, role-aware collaboration in UAV swarms. ...

July 7, 2025 · 3 min · Zelina
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Mind Over Modules: How Smart Agents Learn What to See—and What to Be

In the race to build more autonomous, more intelligent AI agents, we’re entering an era where “strategy” isn’t just about picking the next move—it’s about choosing the right mind for the job and deciding which version of the world to trust. Two recent arXiv papers—one on state representation in dynamic routing games, the other on self-generating agentic systems with swarm intelligence—show just how deeply this matters in practice. We’re no longer only asking: What should the agent do? We now must ask: ...

June 19, 2025 · 5 min · Zelina