AMGenC generates guaranteed charge-balanced amorphous materials using element noise initialization combined with per-step soft and final discrete projections in a generative model.
Symbolic music generation with non-differentiable rule guided diffusion
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Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
The work formalizes zero-shot symbolic drum editing as LLM reasoning over a drumroll grid notation, evaluates it on a new benchmark with automated symbolic unit tests, and reports up to 68% success across eight models.
citing papers explorer
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AMGenC: Generating Charge Balanced Amorphous Materials
AMGenC generates guaranteed charge-balanced amorphous materials using element noise initialization combined with per-step soft and final discrete projections in a generative model.
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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Not that Groove: Zero-Shot Symbolic Music Editing
The work formalizes zero-shot symbolic drum editing as LLM reasoning over a drumroll grid notation, evaluates it on a new benchmark with automated symbolic unit tests, and reports up to 68% success across eight models.