Corrector
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Related Articles from SNS
MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation
arXiv:2606.09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality. To address this, we propose a novel MeanFlow-based one-step generative corrector (MeCo). MeCo learns a conditional average velocity field to map discriminative estimates directly onto the clean speech manifold in a single step.
PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
Announce Type: replace Abstract: Coupled spatiotemporal forecasting is important for predicting the future evolution of multiple interacting dynamical systems, such as in climate models. However, existing methods are severely constrained by the persistent bottleneck of compounding errors. In coupled systems, errors from each subsystem simulator propagate and amplify one another, a phenomenon we term Reciprocal Error Amplification, leading to a rapid collapse of long-range predictions.
Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
arXiv:2601.04791v4 Announce Type: replace Abstract: While latent diffusion models (LDMs) have emerged as powerful priors for inverse problems, existing LDM-based solvers frequently suffer from instability. In this work, we first identify the instability as a discrepancy between the solver dynamics and stable reverse diffusion dynamics learned by the diffusion model, and show that reducing this gap stabilizes the solver. Building on this, we introduce \textit{Measurement-Consistent Langevin...
BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers
arXiv:2606.02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion? Recent studies indicate that this may be the case. In combination with predictor-corrector samplers, uniform-state diffusion models (USDMs) produce samples of higher-quality than masked diffusion models (MDMs), and USDMs equal or outperform MDMs in downstream tasks, even though they exhibit greater perplexity.
Design and electron optics performance of a MEMS electrostatic electron monochromator
arXiv:2606.09423v1 Announce Type: new Abstract: Monochromators are routinely used in Transmission Electron Microscopy and Electron Energy Loss Spectroscopy, to improve both spatial and energy resolution. State-of-the-art monochromators, however, are complex instruments that typically require additional electron optical correctors, limiting their implementation to the high-end, most expensive microscopes. Miniaturized monochromation relying on purely electrostatic fringe fields has recently...
A Multi-Invariant Preserving Discrete Gradient Methods
arXiv:2605.30827v1 Announce Type: new Abstract: This work introduces a novel structure-preserving methods for conservative systems based on a predictor-corrector strategy. The framework applies a discrete gradient correction to predictions generated by explicit one-step or multi-step schemes, which preserves nonlinear invariants while maintaining the accuracy order of the original predictor.
CYGNET: Cypher Gate for Neural Execution Triage and Cost Containment
new Abstract: Language models acting as agents over knowledge graphs generate Cypher queries that fail structurally (crashing at the database) or semantically (executing but returning wrong results). We place a pre-execution gate between query generation and a production Neo4j database. The gate validates structure through a four-backend chain culminating in execution against a mirror graph at 5.6 ms median latency.
Methods for Inferring Interaction Potentials from Cross-Linking Mass Spectrometry Data
Announce Type: new Abstract: Cross-linking mass spectrometry (XL-MS) has emerged as a powerful quantitative technique for probing intra-protein structural information as well as protein-protein interactions at an unprecedented scale. XL-MS data yield information on the pairwise spatial proximity of proteins through inter-molecular linkers. However, systematic methods for adapting such data for coarse-grained interacting particle models remain limited.
Spherical Flows for Sampling Categorical Data
arXiv:2605.05629v3 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $\mathbb S^{d-1}$. There the von Mises-Fisher (vMF) distribution induces a natural noise process and admits a closed-form conditional score. The conditional velocity is in general intractable.
A Perturbed q-Tsallis Self-Concordant Barrier for Spectrally Robust Semidefinite Programming
Announce Type: cross Abstract: We introduce and analyse a perturbed $q$-Tsallis barrier for semidefinite programming (SDP), defined as a spectral perturbation of the classical log-det barrier on the cone of positive definite matrices. The barrier introduces eigenvalue-adaptive stiffening through a Tsallis-type matrix-power term controlled by parameters $q>1$ and $\eta\geq0$. Our main theoretical contribution is a sharp characterisation of the differential self-concordance regime of the...