arXiv:2602.18534v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for program translation, particularly for migrating systems code to memory-safe languages such as Rust. However, existing approaches struggle when source programs depend on external libraries: LLMs frequently hallucinate non-existent target APIs and fail to generate call-enabling imports; moreover, validating semantic equivalence is challenging when the code manipulates opaque, library-defined types. We present a translation and validation framework for translating Go projects with external dependencies to Rust. […]

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arXiv:2505.08683v3 Announce Type: replace Abstract: Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortized Bayesian Inference (ABI) can become computationally challenging. While ABI enables fast inference after training, generating sufficient training data still requires thousands of model simulations, which is infeasible for expensive models. Surrogate models offer a solution by providing approximate simulations at a lower computational cost, allowing the generation of […]

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Sunday panic attacks are the hidden reality for many data leaders. This story explores the “savior complex,” the cycle of burnout, and why we struggle to find rhythm. It challenges the idea that better tools can fix personal patterns and asks the hard question: Can we do transformational work without destroying ourselves? You aren’t broken; you just care too much without boundaries.

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arXiv:2403.05281v2 Announce Type: replace Abstract: Exploring the dependence between covariates across distributions is crucial for many applications. Copulas serve as a powerful tool for modeling joint variable dependencies and have been effectively applied in various practical contexts due to their intuitive properties. However, existing computational methods lack the capability for feasible inference and sampling of any copula, preventing their widespread use. This paper introduces an innovative quasi-random sampling approach for copulas, utilizing generative adversarial networks (GANs) and space-filling […]

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Hi everyone, I’m currently working on my thesis and I’m specifically looking for research papers or resources on solving emergency transportation or emergency dispatch problems (such as ambulance routing, dynamic fleet management, or emergency logistics) using Quantum Multi-Agent Reinforcement Learning (QMARL). My focus is on integrating quantum computing techniques (e.g., variational quantum circuits, quantum-enhanced policy/value functions, hybrid quantum-classical models) within a multi-agent RL framework to handle dynamic, stochastic, and decentralized decision-making settings. Despite extensive searching, I haven’t found […]

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arXiv:2601.00172v1 Announce Type: new Abstract: Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradient-based training and memory bottlenecks. Reservoir Computing (RC) mitigates these challenges by replacing backpropagation with fixed recurrent layers and a convex readout optimization, yet conventional RC architectures still scale poorly with input dimensionality. We introduce a Sequential Reservoir Computing (Sequential RC) architecture that decomposes a large reservoir into a series of smaller, interconnected […]

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arXiv:2602.23387v1 Announce Type: new Abstract: Recent advancements in Large Audio Language Models (LALMs) have demonstrated exceptional performance in speech recognition and translation. However, existing models often suffer from a disconnect between perception and expression, resulting in a robotic “read-speech” style that lacks the spontaneity and emotional resonance of real human interaction. In this report, we introduce Hello-Chat, an end-to-end audio language model designed for realistic social scenarios. By leveraging a massive dataset of real-life conversations and employing a […]

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arXiv:2603.01346v1 Announce Type: cross Abstract: We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data. Prior work has shown that such marginal-by-marginal guarantees are possible for “most” marginals, with respect to an arbitrary fixed and known measure, but not more generally. We discover that this failure can be attributed to an “indistinguishability” phenomenon: There are marginals which cannot be […]

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Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance and struggle with modality/task discrepancies, as well as model heterogeneity. To address these challenges, we propose FedAFD, a unified MFL framework that enhances client and server learning. On the client side, we introduce a bi-level adversarial alignment strategy to align local and global […]

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