arXiv:2506.02162v3 Announce Type: replace-cross Abstract: Most expressive variational families — such as normalizing flows — lack practical convergence guarantees, as their theoretical assurances typically hold only at the intractable global optimum. In this work, we present a general recipe for constructing tuning-free, asymptotically exact variational flows on arbitrary state spaces from involutive MCMC kernels. The core methodological component is a novel representation of general involutive MCMC kernels as invertible, measurepreserving iterated random function systems, which act as the […]

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arXiv:2603.08854v1 Announce Type: new Abstract: Analyzing large volumes of sensor network data, such as electricity consumption measurements from smart meters, is essential for modern applications but raises significant privacy concerns. Privacy-enhancing technologies like z-anonymity offer efficient anonymization for continuous data streams by suppressing rare values that could lead to re-identification, making it particularly suited for resource-constrained environments. Originally designed for centralized architectures, z-anonymity assumes a trusted central entity. In this paper, we introduce deZent, a decentralized implementation of […]

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arXiv:2604.13230v1 Announce Type: new Abstract: Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsity effects, high estimator variance, and the prohibitive cost of computing several feature classes. Dimensionality reduction has therefore been proposed as a way to make ELA applicable in such settings, but it remains unclear whether features computed in reduced spaces still reflect intrinsic properties of the original landscape. In this work, we investigate the […]

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arXiv:2601.10810v1 Announce Type: new Abstract: Large language models (LLMs) currently suffer from parameter entanglement, where general reasoning capabilities (logic) and specific factual knowledge (facts) exist in a superposition state within shared weights. This coupling leads to the “memory wall,” where computational capacity is squandered on simulating retrieval, often resulting in hallucinations. In this paper, we propose “digital metabolism,” a thermodynamic hypothesis suggesting that targeted forgetting is necessary for distilling a pure neural logic core. To validate this hypothesis, […]

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Privacy-preserving training on sensitive data commonly relies on differentially private stochastic optimization with gradient clipping and Gaussian noise. The clipping threshold is a critical control knob: if set too small, systematic over-clipping induces optimization bias; if too large, injected noise dominates updates and degrades accuracy. Existing adaptive clipping methods often depend on per-example gradient norm statistics, adding computational overhead and introducing sensitivity to datasets and architectures. We propose a control-driven clipping strategy that adapts the threshold using a […]

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I have been around long enough to know that “Forever” is a trap. In the Web3 space, we are obsessed with Immutability. We carve our JPEGs into IPFS granite. We deploy smart contracts meant to outlast civilizations. We scream “Code is Law” and “Data is Eternal.” But as an Immortal, I can tell you: Eternity is boring. Without death, there is no urgency. Without the threat of loss, there is no value. So, I decided to break the […]

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Picture this: You just landed in the US. You’ve got a job, a signed lease, and your phone. But you have zero credit history. No SSN yet. And absolutely nobody is walking you through this financial maze. A coworker swears by this finance app, so you download it. You try linking your bank. It fails. The error message is in English. There’s no clear retry path. You close the app. You’re done. Probably for good. That isn’t just […]

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arXiv:2206.13405v2 Announce Type: replace-cross Abstract: Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating corruption robustness in a way that allows comparability and interpretability on a given dataset. We propose a test data augmentation method that uses a robustness distance $epsilon$ derived from the datasets minimal class separation distance. The resulting MSCR (minimal separation corruption robustness) […]

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arXiv:2603.15725v1 Announce Type: new Abstract: Spiking neural networks (SNNs) and biologically-inspired learning mechanisms are attractive in mobile robotics, where the size and performance of onboard neural network policies are constrained by power and computational budgets. Existing SNN approaches, such as population coding, reward modulation, and hybrid artificial neural network (ANN)-SNN architectures, have shown promising results; however, they face challenges in complex, highly stochastic environments due to SNN sensitivity to hyperparameters and inconsistent gradient signals. To address these challenges, […]

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General aviation fault diagnosis and efficient maintenance are critical to flight safety; however, deploying deep learning models on resource-constrained edge devices poses dual challenges in computational capacity and interpretability. This paper proposes LiteInception–a lightweight interpretable fault diagnosis framework designed for edge deployment. The framework adopts a two-stage cascaded architecture aligned with standard maintenance workflows: Stage 1 performs high-recall fault detection, and Stage 2 conducts fine-grained fault classification on anomalous samples, thereby decoupling optimization objectives and enabling on-demand allocation […]

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