Every idea extracted from recent arXiv mathematics papers — verified and unverified. Click an idea to open its full card; badges show the empirical verdict.
Train a categorical denoiser for the clean token but convert its output analytically into the reverse CTMC jump rates using the exact forward transition kernel. This separates the easy-to-learn clean-token posterior from the quantity required by the reverse process and should keep the uniform-diffusion ELBO finite at initialization, unlike direct denoiser substitution.
Use the envelope's phase transition to choose whether clipping should primarily control update energy or preserve the raw gradient and reduce clipping bias. In the energy-dominated regime, regulate the retained update energy; in the bias-dominated regime, regulate the removed-gradient residual and monitor rare outliers explicitly.
Replace a recurrent token-mixing operation with a finite-state carrier scan over binary or quantized token features. The local transition table is constrained to conserve a scalar token weight and to be bijective, making the mixer reversible, constant-memory, and less prone to activation drift than a generic recurrent layer.
Estimate the temporal spectrum of each sequence channel using a locally private procedure, then apply a regularized inverse-square-root spectral filter before the sequence enters attention or an SSM. The filter removes predictable low-frequency or narrow-band redundancy while avoiding unstable amplification at frequencies where the private estimate is small.