Claims Browser

Discrete diffusion models have become a strong, widely adopted class of generators for sequence data

"Discrete diffusion models have become a strong, widely adopted class of generators for sequence data"

Training-free steering of discrete diffusion models toward downstream rewards is increasingly important

"steering them toward a downstream reward at inference time, without any retraining, is increasingly important"

Current guided proposal methods have a weakness in estimating gradients from single noisy samples

"the guided proposal estimates its gradient from a single noisy sample"

Current search methods have a weakness in resampling particles at fixed temperature that ignores reward distribution across denoising steps

"the search then resamples particles at a fixed temperature that ignores how rewards spread across each denoising step"

GRAS method achieves best training-free reward across regulatory DNA and protein design, outperforming prior training-free methods and matching or surpassing reward-fine-tuned models

"across regulatory DNA and protein design it attains the best training-free reward, outperforming prior training-free methods and matching or surpassing a reward-fine-tuned model"

GRAS remains effective even for non-differentiable rewards

"it remains effective even for non-differentiable rewards"

Rao-Blackwellized reveal lowers estimator variance for differentiable rewards and leave-one-out baseline works for non-differentiable rewards

"we lower the estimator variance with a Rao-Blackwellized reveal for differentiable rewards and a leave-one-out baseline for non-differentiable ones"

The Descriptive-Complexity Information Criterion (DCIC) achieves selection consistency through approximation-error separation without requiring RIP-type conditions, even under sub-Weibull noise

"Under sub-Weibull noise, we establish selection consistency through approximation-error separation without relying on RIP-type conditions"

DCIC provides nonasymptotic oracle risk bounds that remain valid even under model misspecification

"nonasymptotic oracle risk bounds that remain valid under model misspecification"

The coding principle enables placing heterogeneous model classes on a common complexity scale with minimal additional computational cost for class identification

"The same coding principle places heterogeneous classes on a common complexity scale at a small additional class-identification cost"

Large penalties in the complexity-guided search yield polynomial-size retained search regions with high probability

"Large penalties yield polynomial-size retained search regions with high probability"

Numerical experiments demonstrate stable support recovery and favorable estimation performance under strong predictor dependence and model-class uncertainty

"Numerical experiments illustrate stable support recovery and favorable estimation performance under strong dependence and model-class uncertainty"

Human evaluation for non-verifiable tasks is reliable but expensive, while automatic metrics are scalable but often biased

"Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased."

Prediction-powered evaluation provides data-efficient system comparisons that are provably unbiased by combining limited human judgments with large-scale automatic scores

"we propose prediction-powered evaluation, a framework that combines limited human judgments with large-scale automatic scores to obtain data-efficient system comparisons that are provably unbiased."

PPSR yields more discriminative and stable metric rankings than existing system-level meta-metrics

"PPSR directly targets metric utility for prediction-powered evaluation and yields more discriminative and stable metric rankings than existing system-level meta-metrics."

Automatic metrics should be reframed as tools for reducing human annotation cost rather than replacing human judgment

"our new paradigm reframes automatic metrics as tools for reducing human annotation cost rather than replacing human judgment"

The prediction-powered evaluation framework applies broadly to non-verifiable tasks

"applies broadly to non-verifiable tasks"

Physical reservoir computing systems can implement causal information filtering through asymmetric coupling anisotropy, creating deterministic upstream-to-downstream information flow

"We demonstrate a physical mechanism for causal information filtering in a physical reservoir computing (PRC) by exploiting asymmetric coupling anisotropy. Using a network of coupled Duffing oscillators, we show that the directionality of internal coupling induces a spatial gradient in the effective potential, establishing a deterministic upstream-to-downstream information flow."