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Encoding rotation symmetries into multi-agent reinforcement learning boosts performance in vehicle-to-infrastructure systems by over 50% compared to standard approaches.
Telecom World Models fuse the flexibility of LLMs with the fidelity of Digital Twins, enabling uncertainty-aware predictive planning that existing approaches can't match.
