论文

对现有图神经网络模型进行了详细综述，系统性地归纳了其应用，并提出了四个有待解决的未来研究方向A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.

提出 BERTopic，一种通过开发类内 TF-IDF 变体来提取一致性主题表示，从而扩展主题建模流程的主题模型BERTopic is presented, a topic model that extends the process of topic modeling by extracting coherent topic representation through the development of a class-based variation of TF-IDF.

提出一种从校正后的图像对中提取深度信息的方法，使用卷积神经网络在小图像块上学习相似性度量，并针对该任务考察了两种网络架构：一种面向速度优化，另一种面向精度优化This work presents a method for extracting depth information from a rectified image pair by learning a similarity measure on small image patches using a convolutional neural network and examines two network architectures for this task: one tuned for speed, the other for accuracy.

一篇全面综述，旨在深入理解 Few-shot Learning，并从三个维度对 FSL 方法进行分类：数据层面——利用先验知识扩充监督经验；模型层面——利用先验知识缩小假设空间规模；算法层面——利用先验知识改变在给定假设空间中对最优假设的搜索方式A thorough survey to fully understand Few-shot Learning and categorize FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which uses prior knowledge to reduce the size of the hypothesis space; and algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space.

在训练过程中，Random Erasing 在图像中随机选择一个矩形区域并以随机值擦除其像素，在图像分类、目标检测与行人重识别任务中相较于强基线均带来稳定提升In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values and yields consistent improvement over strong baselines in image classification, object detection and person re-identification.

提出 CALVER (Causal Axiom-Level VERification)，一种无需训练的对称验证器，根据 Pearl 的因果准则（包括 d-分离、backdoor 调整与干预）对结构化 trace 评分，并在不参考标准答案的情况下选择得分最高的候选。This work introduces CALVER (Causal Axiom-Level VERification), a training-free symbolic verifier that scores structured traces against Pearl's causal criteria, including -separation, backdoor adjustment, and intervention, and selects the highest-scoring candidate without consulting a reference answer.

提出 ReflectRL，一个轻量级即插即用框架，在 on-policy 训练中从 Golden Negative Trajectories 中学习：先利用这些 trajectory 引出 Reflective Reasoning，再通过 Reflective-to-Direct Policy Transition 将所学到的推理行为迁移回 Direct Reasoning。ReflectRL is proposed, a lightweight plug-and-play framework that learns from Golden Negative Trajectories during on-policy training, and first uses these trajectories to elicit Reflective Reasoning, then applies Reflective-to-Direct Policy Transition to transfer the acquired reasoning behavior back to Direct Reasoning.

K-EXAONE 2.0 在 K-EXAONE 基础上进一步提升，并保持与开源权重模型的竞争力，其最大提升体现在 agentic coding 与长上下文理解上，在长上下文检索与安全性方面优势最为明显。K-EXAONE 2.0 improves over K-EXAONE and remains competitive with open-weight models, showing its largest gains in agentic coding and long-context understanding and its clearest strengths in long-context retrieval and safety.

提出 MultiPathFormer，一种自回归基础模型，将每条发射端–接收端链路表示为连续值路径 token 的有序序列，并通过下一路径预测进行预训练，证明路径级预训练可学习无线传播的可复用表征。MultiPathFormer, an autoregressive foundation model that represents each transmitter-receiver link as an ordered sequence of continuous-valued path tokens and pretrains with next-path prediction, is presented, showing that path-level pretraining can learn reusable representations of wireless propagation.

实验表明，在 Ego2Robot 合成数据与机器人数据上的联合预训练在多种扰动类型上一致提升分布外泛化能力，并在真实机器人部署中得到验证。Experiments show that joint pretraining on Ego2Robot-synthesized and robot data consistently improves out-of-distribution generalization across multiple perturbation types, with benefits validated on real-robot deployment.

文章介绍了一种自监督视觉表征模型 BEiT（Bidirectional Encoder representation from Image Transformers），在图像分类和语义分割上的结果表明，该模型取得了与先前预训练方法相当的竞争性结果。A self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers, is introduced, and results on image classification and semantic segmentation show that the model achieves competitive results with previous pre-training methods.

本文对度量学习文献进行了系统综述，阐述了每种方法的优缺点，并介绍了近期涌现的一系列强大替代方法，包括非线性度量学习、相似性学习与局部度量学习。A systematic review of the metric learning literature is proposed, highlighting the pros and cons of each approach and presenting a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning.

本文提出一种多任务深度学习的原则性方法，通过考虑各任务的同方差不确定性来加权多个损失函数，从而在分类与回归场景下同时学习具有不同单位或尺度的多种量。A principled approach to multi-task deep learning is proposed which weighs multiple loss functions by considering the homoscedastic uncertainty of each task, allowing us to simultaneously learn various quantities with different units or scales in both classification and regression settings.

设计了一种类型化的领域特定语言（DSL），通过一组可逆算子捕获重复区域、浮点域等常见张量结构，将无损张量压缩建模为程序合成问题。A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.

基于该三轴框架，系统梳理代表性方法，追踪持续学习的演进趋势，并讨论由此引发的关键挑战、更广泛的影响以及未来方向。Anchored by this tri-axial framework, representative methods are systematically surveyed, the ongoing transition of continual learning is traced, and the key challenges, broader implications, and future directions arising from this paradigm shift are discussed.

结果表明，使用人类反馈进行微调是使语言模型与人类意图对齐的一个有前景的方向，在真实性方面有所提升，并减少了有毒输出的生成，同时在公开 NLP 数据集上的性能回归极小。The results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent and showing improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.

GaussianSelector 在选择质量上与 SOTA 的多视角 SAM 方法相当，同时所需交互视角显著更少、计算开销明显更低，因而非常适合真实场景下 human-in-the-loop 的 3D 场景编辑与 3D 资产提取。GaussianSelector achieves competitive selection quality against state-of-the-art multi-view SAM-based methods, while requiring significantly fewer interaction views and substantially lower computational overhead, which makes it well suited for human-in-the-loop 3D scene editing and 3D asset extraction in real-world deployment scenarios.

本文详细阐述了驱动 PyTorch 实现的原则及其在架构中的体现，并解释了 runtime 关键组件的精心且务实的实现如何使其协同工作以获得出色的性能。This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.

本文提出了一种基于迭代模型平均的深度网络联邦学习实践方法，并进行了广泛的实证评估，考虑了五种不同的模型架构和四个数据集。This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.

本文提出了一种通过创建在所有边缘设备之间全局共享的小型数据子集来改进非独立同分布数据训练的策略，并表明在 CIFAR-10 数据集上，仅共享 5% 的全局数据即可将准确率提升 30%。This work presents a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices, and shows that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

采用迭代的在线训练模式，按周节奏用新的人类反馈数据更新偏好模型与 RL 策略，并发现 RL 奖励与策略相对其初始化的 KL 散度平方根之间近似呈线性关系。An iterated online mode of training, where preference models and RL policies are updated on a weekly cadence with fresh human feedback data, and a roughly linear relation between the RL reward and the square root of the KL divergence between the policy and its initialization is identified.

提出 YOLO-PEFT，一个结构感知的框架，将 adapter 的放置建模为可审计的约束规划问题，以显式、可审查的规划取代手工对目标模块的试错，同时保留已验证的 train-save-merge-export 路径。YOLO-PEFT is proposed, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem that replaces manual target-module trial and error with explicit, inspectable planning while preserving verified train-save-merge-export paths.

本文是一项关于如何提升蒸馏训练效率的实践研究，围绕两项系统贡献展开，并提出一种融合的 chunked KL loss，使峰值内存随序列长度线性增长。A practitioner's study of how to make distillation training efficient is presented, organised around two systems contributions, and a fused, chunked KL loss is introduced, making peak memory linear in the sequence length.

研究发现，微调后的 AO 可能变成概念特异的 anti-reader：它们会选择性地无法恢复自身训练过程中持续存在的概念，从而对习得的可解释性接口提出可靠性担忧。It is found that fine-tuned AOs can become concept-specific anti-readers: they selectively fail to recover the concept persistently present during their own training, raising a reliability concern for learned interpretability interfaces.

Manifold Mixup 在监督学习、对单步对抗攻击的鲁棒性、半监督学习以及留出样本的负对数似然（NLL）上，相较强基线均取得了大幅提升。Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.

提出 Decoupling CLI Agent Scaffolding（DCAS），一种后端替换的拦截层，可在不修改 scaffold 的前提下，在任意 CLI scaffold 与任意后端模型之间路由 API 流量，从而支持跨 scaffold 评估与具备规划感知的轨迹采集。Decoupling CLI Agent Scaffolding (DCAS) is introduced, a backend-substitution interception layer that routes API traffic between any CLI scaffold and any backend model without modifying the scaffold, enabling cross-scaffold evaluation and planning-aware trajectory collection.

对抗性 Fréchet 距离 (AdvFD) 用经过校准的对抗性学习表示来补充 FD-Loss 中的静态表示目标，通过对抗方式最大化真实样本与生成样本之间的 Fréchet 差异；同时引入真实特征白化，对尺度与协方差几何进行归一化，从而稳定极小极大优化。Adversarial Fr\'echet Distance (AdvFD), which complements the static representation targets in FD-Loss with a calibrated adversarially learned representation that adversarially maximizes the Fr\'echet discrepancy between real and generated samples, and introduces real-feature whitening, which normalizes its scale and covariance geometry and stabilizes the min--max optimization.

本文研究基于开源大语言模型的无参考多语言机器翻译后训练，发现 on-policy 蒸馏能够达到但无法超越结合 checkpoint 插值的强化学习所确立的质量前沿。This work studies reference-free post-training for multilingual machine translation with open large language models and finds that on-policy distillation reaches, but does not surpass, the quality frontier achieved by RL with checkpoint interpolation.

本文引入静止状态 (rest-state) 公式化方法，从单一闭合构型重建关节物体——这是一种固有的不适定设定，几何、语义与运动先验在此弥补运动线索的缺失。This work introduces a rest-state formulation that reconstructs articulated objects from a single closed configuration, an inherently ill-posed setting where geometry, semantics, and motion priors compensate for the absence of motion cues.

本文提出 UniMoMo，一种后训练压缩框架，将其形式化为约束图粗化问题，并引入分层自适应保护机制，根据路由暴露度限制对高流量 expert 的合并。UniMoMo, a post-training compression framework formulated as a constrained graph coarsening problem, is introduced, and a layer-adaptive protection mechanism that restricts the merging of high-traffic experts based on their routing exposure is introduced.

本工作提出在海量文本语料上使用新的自监督目标 PEGASUS 对大型 Transformer 编码器-解码器模型进行预训练，并证明其在所有 12 个下游数据集上按 ROUGE 分数衡量均取得 SOTA 性能This work proposes pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective, PEGASUS, and demonstrates it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores.

从算法建模、应用和理论分析角度对 MTL 的综述，给出了 MTL 的定义，并将不同 MTL 算法分为五类：特征学习方法、低秩方法、任务聚类方法、任务关系学习方法和分解方法A survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses, which gives a definition of MTL and classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach,task relation learning approach and decomposition approach.

较为全面地列举了基于群体智能、生物启发、物理启发和化学启发（按灵感来源分类）的所有算法，这些算法已成为解决实际问题的流行工具A relatively comprehensive list of all the algorithms based on swarm intelligence, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration, that have become popular tools for solving real-world problems.

提出了能够平衡"延迟告警的上下文敏感成本"与"打断成本"的模型与推理流程，并通过对用户活动与通知内容的分析，描述了在不确定性下推理此类成本所面临的挑战Models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption are presented and the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications is described.

本文提供了参数空间探索能够改进 LLM 强化学习的证据，并提出称为扰动参数策略优化（3PO）的方法族，使用不同的采样策略和不同的 rollout 分组进行 reward 估计。Evidence that parameter-space exploration can improve reinforcement learning for LLMs is presented, and a family of methods called Perturbed Parameter Policy Optimization (3PO) is introduced which use different sampling strategies and different rollout grouping for reward estimation.

本工作训练了一个预测的计算最优模型 Chinchilla，使用与 Gopher 相同的计算预算，但参数量为 70B、数据量为 4 倍，达到 SOTA 平均准确率，比 Gopher 提升超过 7%。This work trains a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\times$ more more data, and reaches a state-of-the-art average accuracy, greater than a 7% improvement over Gopher.