arXiv:2604.02351v1 Announce Type: new Abstract: Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. While common mitigation strategies such as periodic retraining and recalibration aim to preserve performance, they typically focus on average metrics evaluated at isolated time points and do not explicitly model how reliability evolves during deployment. We propose a deployment-centric framework that treats reliability as a dynamic state composed of discrimination and calibration. The […]

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arXiv:2604.05030v1 Announce Type: new Abstract: We present Phase-Associative Memory (PAM), a recurrent sequence model in which all representations are complex-valued, associations accumulate in a matrix state $S_{t}$ $in$ $mathbb{C}^{d times d}$ via outer products, and retrieval operates through the conjugate inner product $K_t^* cdot Q_t / sqrt{d}$. At $sim$100M parameters on WikiText-103, PAM reaches validation perplexity 30.0, within $sim$10% of a matched transformer (27.1) trained under identical conditions, despite $4times$ arithmetic overhead from complex computation and no custom […]

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We integrate static taint analysis with dynamic fuzzing to target high-impact kernel code paths. A pruning mechanism removes irrelevant taint propagation, while symbolic constraints are applied only to tainted regions to control overhead. Evaluated on 18 kernel subsystems, the hybrid fuzzer achieves 44% more taint-relevant path hits, identifying 13 bugs, including buffer overflows and pointer dereferences. Symbolic overhead remains limited (≤18%) through selective propagation. This hybrid design efficiently directs fuzzing toward semantically meaningful kernel logic, demonstrating a productive […]

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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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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results Abstract Graph clustering is a fundamental problem in machine learning. Deep learning […]

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Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group Relative Policy Optimization treat sampled trajectories as independent chains, assigning uniform credit to all steps in each chain and ignoring the existence of critical steps that may disproportionally impact reasoning outcome. In this paper, we propose T-STAR(Tree-structured Self-Taught Agent Rectification), a framework that recovers the latent correlated reward structure across seemingly independent trajectories. Specifically, we consolidate trajectories […]

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Nonstationary time series forecasting suffers from the distribution shift issue due to the different distributions that produce the training and test data. Existing methods attempt to alleviate the dependence by, e.g., removing low-order moments from each individual sample. These solutions fail to capture the underlying time-evolving structure across samples and do not model the complex time structure. In this paper, we aim to address the distribution shift in the frequency space by considering all possible time structures. To […]

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This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challenges in dynamic wireless networked environments. Unlike conventional RMAB models, our model allows each user (arm) to have distinct and stringent performance constraints, such as energy limits, activation limits, or age of information minimums, enabling the capture of diverse objectives including fairness and efficiency. To find the optimal resource allocation policy, we propose a new Penalty-Optimal Whittle (POW) index policy. The […]

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arXiv:2602.10157v1 Announce Type: new Abstract: Encryption has been commonly used in network traffic to secure transmission, but it also brings challenges for malicious traffic detection, due to the invisibility of the packet payload. Graph-based methods are emerging as promising solutions by leveraging multi-host interactions to promote detection accuracy. But most of them face a critical problem: Graph Drift, where the flow statistics or topological information of a graph change over time. To overcome these drawbacks, we propose a […]

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