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arXiv:2602.01247v1 Announce Type: new Abstract: Brain-to-speech decoding models demonstrate robust performance in vocalized, mimed, and imagined speech; yet, the fundamental mechanisms via which these models...
arXiv:2602.01253v1 Announce Type: new Abstract: Requirements traceability, the process of establishing and maintaining relationships between requirements and various software development artifacts, is paramount for ensuring...
arXiv:2602.01257v1 Announce Type: new Abstract: Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and localization accuracy. However,...
arXiv:2602.01260v1 Announce Type: new Abstract: Offline reinforcement learning (RL) enables policy learning from static data but often suffers from poor coverage of the state-action space...
arXiv:2602.01261v1 Announce Type: new Abstract: Electric vehicle (EV) charging infrastructure introduces complex challenges to urban distribution networks, particularly under extreme demand events. A critical barrier...
arXiv:2602.01264v1 Announce Type: new Abstract: Artificial intelligence (AI) is increasingly used to support prognosis in Alzheimer's disease (AD), but adoption remains limited due to a...
arXiv:2602.01265v1 Announce Type: new Abstract: Knowledge distillation (KD) is a machine learning framework that transfers knowledge from a teacher model to a student model. The...
arXiv:2602.01266v1 Announce Type: new Abstract: This paper addresses the challenge of active perception within autonomous navigation in complex, unknown environments. Revisiting the foundational principles of...
arXiv:2602.01267v1 Announce Type: new Abstract: Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component structures. However,...
arXiv:2602.01268v1 Announce Type: new Abstract: Recent monocular foundation models excel at zero-shot depth estimation, yet their outputs are inherently relative rather than metric, limiting direct...
arXiv:2602.01270v1 Announce Type: new Abstract: A fundamental challenge in multi-task reinforcement learning (MTRL) is achieving sample efficiency in visual domains where tasks exhibit substantial heterogeneity...
arXiv:2602.01271v1 Announce Type: new Abstract: Telecommunication networks are increasingly expected to operate autonomously while supporting heterogeneous services with diverse and often conflicting intents -- that...
arXiv:2602.01273v1 Announce Type: new Abstract: Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their heavy inference burden...
arXiv:2602.01274v1 Announce Type: new Abstract: Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs) without sacrificing accuracy....
arXiv:2602.01276v1 Announce Type: new Abstract: Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying...
arXiv:2602.01277v1 Announce Type: new Abstract: Autonomous driving systems require robust lane perception capabilities, yet existing vision-based detection methods suffer significant performance degradation when visual sensors...
arXiv:2602.01278v1 Announce Type: new Abstract: Accurate extraction of rural roads from high-resolution remote sensing imagery is essential for infrastructure planning and sustainable development. However, this...
arXiv:2602.01279v1 Announce Type: new Abstract: Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underestimate...
arXiv:2602.01283v1 Announce Type: new Abstract: Multilingual safety remains significantly imbalanced, leaving non-high-resource (NHR) languages vulnerable compared to robust high-resource (HR) ones. Moreover, the neural mechanisms...
arXiv:2602.01284v1 Announce Type: new Abstract: As deepfake videos become increasingly difficult for people to recognise, understanding the strategies humans use is key to designing effective...
arXiv:2602.01285v1 Announce Type: new Abstract: Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and...
arXiv:2602.01288v1 Announce Type: new Abstract: Entropy-based confidence signals are increasingly leveraged to improve reasoning in large language models (LLMs), yet existing approaches treat confidence as...
arXiv:2602.01289v1 Announce Type: new Abstract: Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of...
arXiv:2602.01290v1 Announce Type: new Abstract: Accurate and energy efficient localization remains a key challenge in Wireless Sensor Networks (WSNs), particularly when obstacles affect signal propagation....