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Differentiable environments and backpropagation offer a surprisingly effective alternative to reinforcement learning for AAV trajectory optimization, sidestepping credit assignment problems.
LLMs can orchestrate complex wireless communication optimization tasks by translating natural language intent into actionable spatial constraints, enabling gradient-based solvers to outperform traditional methods without requiring domain-specific fine-tuning.
Achieve near-perfect radio map reconstruction (SSIM 0.9752, PSNR 36.37 dB) from limited data by injecting electromagnetic theory into diffusion models.
