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arXiv:2601.14746v2 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training without sharing raw data in edge environments, but is constrained by limited communication...
arXiv:2601.14776v2 Announce Type: replace Abstract: Recent advances in multi-modal detection have significantly improved detection accuracy in challenging environments (e.g., low light, overexposure). By integrating RGB...
arXiv:2601.14949v2 Announce Type: replace Abstract: With the rapid growth of Web-based academic publications, more and more papers are being published annually, making it increasingly difficult...
arXiv:2601.15040v2 Announce Type: replace Abstract: This paper presents an innovative offshore solution where oil & gas platform clusters are powered by a wind farm and...
arXiv:2601.15124v2 Announce Type: replace Abstract: Graph Foundation Models (GFMs) have emerged as a frontier in graph learning, which are expected to deliver transferable representations across...
arXiv:2601.15165v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively,...
arXiv:2601.15197v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-task...
arXiv:2601.15333v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited...
arXiv:2601.15434v2 Announce Type: replace Abstract: The evolution of digital manufacturing requires intelligent Question Answering (QA) systems that can seamlessly integrate and analyze complex multi-modal data,...
arXiv:2601.15436v2 Announce Type: replace Abstract: We propose a novel way to evaluate sycophancy of LLMs in a direct and neutral way, mitigating various forms of...
arXiv:2601.15505v2 Announce Type: replace Abstract: The quantum hashing bound guarantees that rates up to $1-H(p_I, p_X, p_Y, p_Z)$ are achievable for memoryless Pauli channels, but...
arXiv:2601.15516v2 Announce Type: replace Abstract: The proliferation of XR devices has made egocentric hand pose estimation a vital task, yet this perspective is inherently challenged...
arXiv:2601.15530v2 Announce Type: replace Abstract: Alzheimer's disease (AD), defined as an abnormal buildup of amyloid plaques and tau tangles in the brain can be diagnosed...
arXiv:2601.15532v2 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs) with multi-connectivity (MC) capabilities efficiently and reliably transfer data between terrestrial networks (TNs) and non-terrestrial networks...
arXiv:2601.15609v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central paradigm for turning large language models (LLMs) into reliable problem solvers,...
arXiv:2601.15719v2 Announce Type: replace Abstract: An utterance-level speaker embedding is typically obtained by aggregating a sequence of frame-level representations. However, in real-world scenarios, individual frames...
arXiv:2601.15727v2 Announce Type: replace Abstract: The performance of modern AI systems is fundamentally constrained by the quality of their underlying kernels, which translate high-level algorithmic...
arXiv:2601.15946v2 Announce Type: replace Abstract: Accurate calibration and robust localization are fundamental for downstream tasks in spinning actuated LiDAR applications. Existing methods, however, require parameterizing...
arXiv:2601.15992v2 Announce Type: replace Abstract: With the increasing use of RDF graphs, storing and querying such data using SPARQL remains a critical problem. Current mainstream...
arXiv:2601.16032v2 Announce Type: replace Abstract: High-performance attention kernels are essential for Large Language Models. This paper presents analysis of CuTile-based Flash Attention memory behavior and...
arXiv:2601.16034v2 Announce Type: replace Abstract: Refusal behavior in aligned LLMs is often viewed as model-specific, yet we hypothesize it stems from a universal, low-dimensional semantic...
arXiv:2601.16072v2 Announce Type: replace Abstract: We study Constrained Online Convex Optimization (COCO), where a learner chooses actions iteratively, observes both unanticipated convex loss and convex...
arXiv:2601.16078v2 Announce Type: replace Abstract: One of the core advantages of SE2(3) Lie group framework for navigation modeling lies in the autonomy of error propagation....
arXiv:2601.16199v2 Announce Type: replace Abstract: Machine learning property attestations allow provers (e.g., model providers or owners) to attest properties of their models/datasets to verifiers (e.g.,...