arXiv:2603.08717v1 Announce Type: new Abstract: AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring equitable inference performance across these users requires adaptive and fair learning mechanisms. This paper introduces an online-within-online fair multi-task learning (OWO-FMTL) framework that ensures long-term equity across users. The method combines two learning loops: an outer loop updating the shared model across rounds and an inner loop rebalancing user priorities within each round […]

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arXiv:2603.23736v1 Announce Type: new Abstract: Many scientific systems, such as cellular populations or economic cohorts, are naturally described by probability distributions that evolve over time. Predicting how such a system would have evolved under different forces or initial conditions is fundamental to causal inference, domain adaptation, and counterfactual prediction. However, the space of distributions often lacks the vector space structure on which classical methods rely. To address this, we introduce a general notion of parallel dynamics at a […]

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arXiv:2602.12481v1 Announce Type: new Abstract: Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad’s effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities – arising from proximity, clutter, or crowding – can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce […]

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arXiv:2509.20993v2 Announce Type: replace-cross Abstract: We consider the problem of estimating inverse temperature parameter $beta$ of an $n$-dimensional truncated Ising model using a single sample. Given a graph $G = (V,E)$ with $n$ vertices, a truncated Ising model is a probability distribution over the $n$-dimensional hypercube ${-1,1}^n$ where each configuration $mathbf{sigma}$ is constrained to lie in a truncation set $S subseteq {-1,1}^n$ and has probability $Pr(mathbf{sigma}) propto exp(betamathbf{sigma}^top Amathbf{sigma})$ with $A$ being the adjacency matrix of $G$. We […]

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High-Level Synthesis (HLS) is a pivotal electronic design automation (EDA) technology that enables the generation of hardware circuits from high-level language descriptions. A critical step in HLS is Design Space Exploration (DSE), which seeks to identify high-quality hardware architectures under given constraints. However, the enormous size of the design space makes DSE computationally prohibitive. Although numerous algorithms have been proposed to accelerate DSE, our extensive experimental studies reveal that no single algorithm consistently achieves Pareto dominance across all […]

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Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials science. Equivariant MLIPs achieve state-of-the-art accuracy in a wide range of benchmarks by incorporating equivariant inductive bias. However, the reliance on tensor products and high-degree representations makes them computationally costly. This raises a fundamental question: as quantum mechanical-based datasets continue to expand, can we develop a more compact model to thoroughly exploit high-dimensional atomic interactions? […]

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arXiv:2602.13210v1 Announce Type: new Abstract: Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learning (RL) is a powerful framework, it often encounters difficulties with high-dimensional state spaces and complex environments, leading to substantial computational demands, distributed intelligence, and potentially inconsistent outcomes. Large language models (LLMs), with their extensive pretrained knowledge and advanced reasoning capabilities, offer promising tools to enhance RL in optimizing […]

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Understanding an agent’s goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-oriented policy representation, which may differ from the actor’s true behavior and hinder the accurate recognition of their goal. To address this gap, this paper introduces Goal Recognition Alignment through Imitation Learning (GRAIL), which leverages imitation learning and inverse reinforcement learning to learn one goal-directed policy for each candidate goal directly from (potentially […]

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Digital signatures serves as a crucial cryptographic primitive in an e-governance system for the authentication of citizen-government interactions. Traditional methods (DSA, ECDSA) pose computational overheads at resource-limited endpoints and centralized verification servers. While complex-number cryptography provides theoretical efficiency through the Complex Discrete Logarithm Problem (CDLP), prior works often fail to meet the requirements for real-world applications. This paper advances the knowledge in lightweight cryptography by introducing LDSEGoV, a lightweight digital signature scheme for e-governance infrastructure. The proposed method […]

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