arXiv:2604.00499v1 Announce Type: new
Abstract: To schedule LLM inference, the \textit{shortest job first} (SJF) principle is favorable by prioritizing requests with short output lengths to avoid head-of-line (HOL) blocking. Existing methods usually p…
arXiv:2603.28117v3 Announce Type: replace
Abstract: Livestock growth prediction is essential for optimising farm management and improving the efficiency and sustainability of livestock production, yet it remains underexplored due to limited large-scal…
arXiv:2604.00767v1 Announce Type: new
Abstract: Wearable HAR has improved steadily, but most progress still relies on closed-set classification, which limits real-world use. In practice, human activity is open-ended, unscripted, personalized, and ofte…
arXiv:2512.02418v3 Announce Type: replace-cross
Abstract: Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous dat…
arXiv:2604.00860v1 Announce Type: new
Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large language models. Yet existing methods share a common bl…
arXiv:2604.00918v1 Announce Type: new
Abstract: Spectral graph neural networks learn graph filters, but their behavior with increasing depth and polynomial order is not well understood. We analyze these models in the graph Fourier domain, where each l…
arXiv:2604.01197v1 Announce Type: cross
Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study the problem of learning to generate mixed states on a fini…
arXiv:2508.09281v3 Announce Type: replace
Abstract: Personalized instruction aims to provide learners with support that adapts to their individual knowledge and progress toward learning objectives. Discovering and tracing Knowledge Components (KCs) is…
arXiv:2603.10305v2 Announce Type: replace
Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While…
arXiv:2603.22455v4 Announce Type: replace
Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks. As skill ecosystems grow to tens of thousands of entries, expos…