A team of researchers from Allen Institute for Artificial Intelligence (Ai2), University of Washington and CMU introduce Fluid Benchmarking, an adaptive LLM evaluation method that replaces static accuracy with 2-parameter IRT ability estimation and Fisher-information–driven item selection. By asking only the…
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arXiv:2504.08802v2 Announce Type: replace-cross Abstract: Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scales. Traditionally, these scales are chosen to be dyadic integers, $2^j$.…
arXiv:2509.13267v1 Announce Type: cross Abstract: Discrete Bayesian networks (DBNs) provide a broadly useful framework for modeling dependence structures in multivariate categorical data. There is a vast literature on methods for inferring conditional probabilities and graphical structure in DBNs, but data…
arXiv:2501.02817v3 Announce Type: replace-cross Abstract: Given a pair of time series, we study how the periodicity of one influences the periodicity of the other. There are several known methods to measure the similarity between a pair of time series, but…
arXiv:2509.13232v1 Announce Type: cross Abstract: We revisit policy-gradient optimization for Large Language Models (LLMs) from a single-stream perspective. Prevailing group-based methods like GRPO reduce variance with on-the-fly baselines but suffer from critical flaws: frequent degenerate groups erase learning signals, and…
arXiv:2409.13745v2 Announce Type: replace-cross Abstract: Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model’s training set. Prior MIAs that are built for classification models fail at LLMs,…
arXiv:2509.13130v1 Announce Type: cross Abstract: We make three contributions to conformal prediction. First, we propose fuzzy conformal confidence sets that offer a degree of exclusion, generalizing beyond the binary inclusion/exclusion offered by classical confidence sets. We connect fuzzy confidence sets…
arXiv:2209.01754v4 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed. However, in a number…
