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arXiv:2602.01070v2 Announce Type: replace Abstract: Test-time compute scaling allocates inference computation uniformly, uses fixed sampling strategies, and applies verification only for reranking. In contrast, we...
arXiv:2602.01289v2 Announce Type: replace Abstract: Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of...
arXiv:2602.01317v5 Announce Type: replace Abstract: Decentralized Finance (DeFi) has turned blockchains into financial infrastructure, allowing anyone to trade, lend, and build protocols without intermediaries, but...
arXiv:2602.01428v2 Announce Type: replace Abstract: Watermarking is a principled approach for tracing the provenance of large language model (LLM) outputs, but its deployment in practice...
arXiv:2602.01450v3 Announce Type: replace Abstract: To enable personalized and context-aware interactions, conversational AI systems have introduced a new mechanism: Memory. Memory creates what we refer...
arXiv:2602.01524v2 Announce Type: replace Abstract: This paper proposes a novel hybrid control framework for switched linear parameter-varying (LPV) systems under hysteresis switching logic. By introducing...
arXiv:2602.01560v3 Announce Type: replace Abstract: This study proposes Argument Rarity-based Originality Assessment (AROA), a framework for automatically evaluating argumentative originality in student essays. AROA defines...
arXiv:2602.01696v5 Announce Type: replace Abstract: Transmission line defect detection remains challenging for automated UAV inspection due to the dominance of small-scale defects, complex backgrounds, and...
arXiv:2602.02068v3 Announce Type: replace Abstract: The present work addresses the Cauchy problem for an abstract nonlinear system of coupled hyperbolic equations associated with the Timoshenko...
arXiv:2602.02131v2 Announce Type: replace Abstract: Stacked intelligent metasurfaces (SIM) provide a cost-effective and scalable solution for large-scale antenna communications.However, efficient channel state information acquisition and...
arXiv:2602.02853v2 Announce Type: replace Abstract: Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics...
arXiv:2602.03003v3 Announce Type: replace Abstract: Social choice has become a foundational component of modern machine learning systems. From auctions and resource allocation to the alignment...
arXiv:2602.03098v2 Announce Type: replace Abstract: Expanding multimodal representations to novel modalities is constrained by reliance on large-scale paired datasets (e.g., text-image, text-audio, text-3D, text-molecule), which...
arXiv:2602.03596v2 Announce Type: replace Abstract: Machine learning-based anomaly detection systems are increasingly being adopted in 5G Core networks to monitor complex, high-volume traffic. However, most...
arXiv:2602.03707v3 Announce Type: replace Abstract: Long-horizon omnimodal question answering answers questions by reasoning over text, images, audio, and video. Despite recent progress on OmniLLMs, low-resource...
arXiv:2602.04595v2 Announce Type: replace Abstract: Large Language Models (LLMs) are powerful but incur high memory and computation costs. Quantization is an effective solution, with INT...
arXiv:2602.05165v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing the reasoning capabilities of Large Language Models (LLMs). However,...
arXiv:2602.05220v2 Announce Type: replace Abstract: Current audio foundation models typically rely on rigid, task-specific supervision, addressing isolated factors of audio rather than the whole. In...
arXiv:2602.05495v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on...
arXiv:2602.05695v2 Announce Type: replace Abstract: Large Language Models (LLMs) inference is central to modern AI applications, dominating worldwide datacenter workloads, making it critical to predict...
arXiv:2602.07135v3 Announce Type: replace Abstract: Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex...
arXiv:2602.07754v2 Announce Type: replace Abstract: This study investigates students' perceptions of Artificial Intelligence (AI) grading systems in an undergraduate computer science course (n = 27),...
arXiv:2602.07854v3 Announce Type: replace Abstract: Predictive world models that simulate future observations under explicit camera control are fundamental to interactive AI. Despite rapid advances, current...
arXiv:2602.07883v2 Announce Type: replace Abstract: Agentic systems powered by Large Language Models (LLMs) have demonstrated remarkable potential in tackling complex, long-horizon tasks. However, their efficacy...