用大模型重塑无线网络MAC协议，以动态Stackelberg博弈提升泛化与韧性，告别DRL高成本重训。
arXiv:2510.10895v2 Announce Type: replace Abstract: Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually con…
多模态大语言模型如何提升行人重识别的泛化能力？本文提出全新重排序框架，为计算机视觉领域带来突破性思路。
arXiv:2606.16161v1 Announce Type: new Abstract: Domain Generalizable (DG) person re-identification (Re-ID) has attracted growing research interest due…
评估算法选择模型在真实场景中的泛化能力，揭示理论与实际性能差距的关键研究
arXiv:2606.02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a…
为语音感知大模型引入轻量级层间引导向量，大幅提升跨域泛化能力，适配高效。
arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA…
大模型推理新突破：通过认知对齐后训练，从元思维到执行提升泛化性与可靠性
arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine…
新方法通过差异最小化提升数字病理模型在不同医院数据上的鲁棒性，解决领域偏移难题。
arXiv:2605.25175v1 Announce Type: new Abstract: Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifi…
机器人基础模型新突破：通用姿态预训练让视觉-语言-动作策略泛化能力飙升，已被RSS 2026接收。
arXiv:2602.19710v2 Announce Type: replace-cross Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low trai…
提出ReAlign方法，通过推理对齐表征实现高泛化性的AI生成图像伪造检测
arXiv:2605.16080v1 Announce Type: new Abstract: The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting t…
用形状分析揭示数据增强如何重塑神经网络内部表征几何，为提升泛化能力提供新视角。
arXiv:2605.15306v1 Announce Type: new Abstract: Data augmentation is widely recognized for improving generalization in deep networks, yet its impact o…