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arXiv:2511.05035v2 Announce Type: replace Abstract: Modular composition is the problem of computing the coefficient vector of the polynomial $f(g(x)) \bmod h(x)$, given as input the...
arXiv:2511.05097v2 Announce Type: replace Abstract: Tracking vulnerabilities inherited from third-party open-source software is a well-known challenge, often addressed by tracing the threads of dependency information....
arXiv:2511.05878v2 Announce Type: replace Abstract: Log-based anomaly detection is critical for ensuring the stability and reliability of web systems. One of the key problems in...
arXiv:2511.06563v2 Announce Type: replace Abstract: Adopting artificial intelligence (AI) in radio access networks (RANs) presents several challenges, including limited availability of link-level measurements (e.g., CQI...
arXiv:2511.06893v2 Announce Type: replace Abstract: Time-Series (TS) exhibits pronounced non-stationarity. Consequently, most forecasting methods display compromised robustness to concept drift, despite the prevalent application of...
arXiv:2511.07776v2 Announce Type: replace Abstract: Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes...
arXiv:2511.08825v2 Announce Type: replace Abstract: The value function of a POMDP exhibits the piecewise-linear-convex (PWLC) property and can be represented as a finite set of...
arXiv:2511.09014v2 Announce Type: replace Abstract: As a generalization of Hermite interpolation problem, Birkhoff interpolation is an important subject in numerical approximation. This paper generalizes the...
arXiv:2511.09785v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used to annotate learning interactions, yet concerns about reliability limit their utility. We test...
arXiv:2511.16778v2 Announce Type: replace Abstract: Recently, structure-text contrastive learning has shown promising performance on text-attributed graphs by leveraging the complementary strengths of graph neural networks...
arXiv:2511.17045v3 Announce Type: replace Abstract: We introduce RacketVision, a novel dataset and benchmark for advancing computer vision in sports analytics, covering table tennis, tennis, and...
arXiv:2511.20258v3 Announce Type: replace Abstract: Weight Averaging (WA) has emerged as a powerful technique for enhancing generalization by promoting convergence to a flat loss landscape,...
arXiv:2511.21658v2 Announce Type: replace Abstract: Artificial intelligence-based systems for player risk detection have become central to harm prevention efforts in the gambling industry. However, growing...
arXiv:2512.00208v2 Announce Type: replace Abstract: We present ReactionMamba, a novel framework for generating long 3D human reaction motions. Reaction-Mamba integrates a motion VAE for efficient...
arXiv:2512.00311v2 Announce Type: replace Abstract: Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring...
arXiv:2512.03553v2 Announce Type: replace Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must...
arXiv:2512.05150v2 Announce Type: replace Abstract: Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These...
arXiv:2512.06973v3 Announce Type: replace Abstract: Control Barrier Functions (CBFs) have been used to enforce safety and task specifications expressed in Signal Temporal Logic (STL). However,...
arXiv:2512.07051v2 Announce Type: replace Abstract: Medical image segmentation plays a pivotal role in automated diagnostic and treatment planning systems. In this work, we present DAUNet,...
arXiv:2512.07173v3 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic...
arXiv:2512.11229v2 Announce Type: replace Abstract: Diffusion models have significantly advanced the field of talking head generation (THG). However, slow inference speeds and prevalent non-autoregressive paradigms...
arXiv:2512.13980v2 Announce Type: replace Abstract: This paper proposes a structure-aware decoding method based on large language models to address the difficulty of traditional approaches in...
arXiv:2512.14549v2 Announce Type: replace Abstract: This paper combines autoregressive and masked-diffusion training objectives without any architectural modifications, resulting in flexible language models that outperform single-objective...
arXiv:2512.15036v2 Announce Type: replace Abstract: In real-world applications with large state and action spaces, reinforcement learning (RL) typically employs function approximations to represent core components...