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Unlocking latent topological structures with discrete Morse theory boosts graph diffusion model performance, offering theoretical stability guarantees.
LLM-ACES outperforms traditional methods by leveraging a closed-loop feedback mechanism that iteratively refines both hypotheses and data acquisition, achieving unprecedented accuracy in ODE recovery.
