arXiv:2601.22367v1 Announce Type: new Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $beta>0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset and each $beta$ value. We give the first fully amortized variational approximation to the tempered posterior family $p_beta(theta mid x) propto pi(theta),p(x mid theta)^beta$ by training a single $(x,beta)$-conditioned neural posterior estimator $q_phi(theta […]

Ver mais

arXiv:2604.01345v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses adaptive IRL aims to reconstruct the loss function of a forward learner by passively observing its gradients as it performs reinforcement learning (RL). This paper proposes a novel passive Langevin-based algorithm that achieves adaptive IRL. The key difficulty in adaptive IRL is that the required gradients in the passive algorithm are counterfactual, that is, they are conditioned […]

Ver mais

arXiv:2603.07522v1 Announce Type: new Abstract: Privacy protection and uncertainty quantification are increasingly important in data-driven decision making. Conformal prediction provides finite-sample marginal coverage, but existing private approaches often rely on data splitting, reducing the effective sample size. We propose a full-data privacy-preserving conformal prediction framework that avoids splitting. Our framework leverages stability induced by differential privacy to control the gap between in-sample and out-of-sample conformal scores, and pairs this with a conservative private quantile routine designed to prevent […]

Ver mais

There is a quiet shift happening in software development. For decades, the most valuable skill for a developer was the ability to write code efficiently and correctly. Entire careers were built around mastering frameworks, memorizing patterns, and becoming faster at turning requirements into working software. But the rise of AI coding agents is changing that equation. Today, machines are becoming exceptionally good at the one thing developers spent years mastering: churning out code. Tools like OpenAI’s ChatGPT and […]

Ver mais

arXiv:2511.18060v2 Announce Type: replace Abstract: Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows. Existing algorithmic developments implicitly make use of operator splitting techniques to numerically approximate the WFR partial differential equation, whereby the W flow is evaluated over a given step size and then the FR flow (or vice versa). This works investigates the impact of the order in […]

Ver mais

arXiv:2601.21049v1 Announce Type: new Abstract: User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retrieval under recall noise, where the observed query is drawn from a noisy recall process of a latent target item. To address this, we propose QUARK, a simple yet effective training-free framework for robust retrieval under non-faithful queries. QUARK explicitly models query uncertainty through recovery hypotheses, i.e., multiple […]

Ver mais

arXiv:2601.00267v1 Announce Type: new Abstract: Recent advances in text-to-image diffusion models have demonstrated remarkable generation capabilities, yet they raise significant concerns regarding safety, copyright, and ethical implications. Existing concept erasure methods address these risks by removing sensitive concepts from pre-trained models, but most of them rely on data-intensive and computationally expensive fine-tuning, which poses a critical limitation. To overcome these challenges, inspired by the observation that the model’s activations are predominantly composed of generic concepts, with only a […]

Ver mais

arXiv:2604.14166v1 Announce Type: new Abstract: Mapping Cyber Threat Intelligence (CTI) text to MITRE ATT&CK technique IDs is a critical task for understanding adversary behaviors and automating threat defense. While recent Retrieval-Augmented Generation (RAG) approaches have demonstrated promising capabilities in this domain, they fundamentally rely on a flat retrieval paradigm. By treating all techniques uniformly, these methods overlook the inherent taxonomy of the ATT&CK framework, where techniques are structurally organized under high-level tactics. In this paper, we propose H-TechniqueRAG, […]

Ver mais

arXiv:2603.01975v1 Announce Type: new Abstract: We introduce a supervised dimensionality reduction methodology for categorical (and discretized mixed-type) data based on a density-matrix construction induced by class-conditional frequencies. Given a labeled dataset encoded in a one-hot survey space, we assemble a frequency matrix whose columns aggregate feature occurrences within each class, and define a normalized Gram-type operator that satisfies the axioms of a density matrix. The resulting representation admits an intrinsic rank bound controlled by the number of classes, […]

Ver mais