论文

结果表明，显式且权威的状态建模为可扩展、一致的多智能体世界仿真提供了可行基础。The results show that explicit, authoritative state modeling provides a practical foundation for scalable and consistent multi-agent world simulation.

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.

配对 MEG 掩蔽实验表明，19 个刺激特征中有 15 个有贡献，其中静音、声音强度、元音和声学起始的影响最大；说明缺乏叙事结构的神经活动相比连贯语音下的活动，可恢复的信息更少。Paired MEG occlusion shows that 15 of 19 stimulus features contribute, with the largest effects for silence, sound intensity, vowels, and acoustic onsets, indicating that activity without narrative structure carries less recoverable information than activity during coherent speech.

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.

在三种大语言模型上的实验表明，思维链提示能够在一系列算术、常识和符号推理任务上提升性能。Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks.

本工作提出两种参数削减技术以降低 BERT 的内存占用并提升训练速度，并采用一种聚焦于建模句子间连贯性的自监督损失。This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

本工作提出 BERTScore——一种文本生成自动评估指标，与人类判断的相关性更强，并在模型选择性能上优于现有指标。This work proposes BERTScore, an automatic evaluation metric for text generation that correlates better with human judgments and provides stronger model selection performance than existing metrics.

研究表明，利用变分方法最新进展的深度生成模型与近似贝叶斯推断能够带来显著提升，使生成式方法在半监督学习上极具竞争力。It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

本工作提出一种通过引入自注意力来提取可解释句子嵌入的新模型，使用一个二维矩阵表示嵌入，其中矩阵的每一行关注句子的不同部分。A new model for extracting an interpretable sentence embedding by introducing self-attention is proposed, which uses a 2-D matrix to represent the embedding, with each row of the matrix attending on a different part of the sentence.

本工作指出，这些局部 patch 内部的注意力同样是构建高性能视觉 Transformer 的关键，并探索了一种新架构，即 Transformer iN Transformer (TNT)。It is pointed out that the attention inside these local patches are also essential for building visual transformers with high performance and a new architecture, namely, Transformer iN Transformer (TNT), is explored.

提出 ASGE-RR，一种支持可修订预留的在线 ASGE 控制器：该在线网络控制问题将运行时揭示的工作流调用映射到服务副本与网络路径上，受容量、成本和截止时间约束；研究表明，运行时揭示的工作流结构创造了新的网络控制机会。This work presents ASGE-RR, an online ASGE controller with revisable reservations, an online network-control problem that maps runtime-revealed workflow calls to service replicas and network paths under capacity, cost and deadline constraints and suggests that runtime-revealed workflow structure creates a new network control opportunity.

提出 READ（Reliable Embedding-free Agentic Document-search）：Agent 通过三种确定性操作——归一化词法搜索、结构导航与有界片段读取——直接读取原始文档，这些操作通过 Model Context Protocol 暴露，使轨迹成为可回放的审计轨迹，而非不透明相似度分数。This work proposes READ (Reliable Embedding-free Agentic Document-search), in which an agent reads the raw document through three deterministic operations -- normalized lexical search, structural navigation, and bounded span reads -- exposed over the Model Context Protocol, so a trajectory is a replayable audit trail, not an opaque similarity score.

提出 FactorJEPA，将世界结构作为一等预测原语，并通过 visibility gate 与分离的子空间来组合布局、实体与交互，以保留部分可观测的 Agent 并抑制跨因子捷径。FactorJEPA is introduced, which makes world structure a first-class predictive primitive, and composes layout, entities, and interactions, using a visibility gate and separated subspaces to preserve partially observed agents and discourage cross-factor shortcuts.

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.

这项系统性研究在数十项语言理解任务上比较了预训练目标、架构、无标注数据集、迁移方法及其他因素，并在涵盖摘要、问答、文本分类等的许多基准上取得了 SOTA 结果。This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

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 mixup，一种通过对样本对及其标签的凸组合来训练神经网络的简单学习原则，提升了 SOTA 神经网络架构的泛化能力。This work proposes mixup, a simple learning principle that trains a neural network on convex combinations of pairs of examples and their labels, which improves the generalization of state-of-the-art neural network architectures.

本文提出了一种通用的无监督学习方法——对比预测编码（Contrastive Predictive Coding），用于从高维数据中提取有用的表征，并在语音、图像、文本和 3D 环境中的强化学习四个不同领域取得了出色的性能。This work proposes a universal unsupervised learning approach to extract useful representations from high-dimensional data, which it calls Contrastive Predictive Coding, and demonstrates that the approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

本文提出了一种双时间尺度更新规则（TTUR），用于在任意 GAN 损失函数下使用 SGD 训练 GAN，并引入了 Frechet Inception Distance（FID），相比 Inception Score 能更好地捕捉生成图像与真实图像之间的相似性。This work proposes a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions and introduces the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score.

名为 PaLM 的 540 亿参数、密集激活的 Transformer 语言模型取得了突破性性能，在一系列多步推理任务上超越了微调后的 SOTA，并在最近发布的 BIG-bench 基准上超越了人类平均水平。A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

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.

研究表明，APE 生成的提示词既可引导模型趋向真实性和/或信息量，也可通过将其前置拼接到标准上下文学习提示词之前来提升少样本学习性能。It is shown that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts.

本文提出 Atlas，一个经过精心设计并预训练的检索增强大语言模型，能以极少训练样例学习知识密集型任务，并研究了文档索引内容的影响，表明该索引可便捷地更新。This work presents Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples, and studies the impact of the content of the document index, showing that it can easily be updated.

提出 PHOENIX（Predictive Health On-orbit Edge Neural Intelligence eXtension），为卫星赋予自主故障推理能力，并在 ESA Anomaly Detection Benchmark 上报告了初步结果。PHOENIX (Predictive Health On-orbit Edge Neural Intelligence eXtension) is proposed to give the satellite its own fault reasoning capability, and preliminary results on the ESA Anomaly Detection Benchmark are reported.

提出 SimWAM，一种简单而有效的 WAM，仅将未来视频预测用作训练时的监督信号，并通过联合 flow matching 协同训练一个预训练视频专家与一个轻量动作专家。SimWAM is presented, a simple yet effective WAM that leverages future-video prediction solely as a training-time supervision signal, and co-trains a pretrained video expert and a lightweight action expert with joint flow matching.

提出 C4，一个受认知启发的成语跨概念创造力评估框架，揭示了当前 MLLM 在通过跨概念关系解码创造性编码语义方面存在的显著差距。C4, a cognition-inspired evaluation framework for Chengyu (Chinese idiom)-based Cross-Concept Creativity, is introduced, exposing a substantial gap in how current MLLMs decode creatively encoded meaning through cross-concept relations.

SMRC-SD（State-Matched Routing and Contextualized Self-Distillation）显式地决定特权轨迹应在何时、以何种方式指导 on-policy student，其表现始终优于无条件的成功全路径蒸馏。State-Matched Routing and Contextualized Self-Distillation (SMRC-SD), which explicitly determines when and how a privileged trajectory should guide an on-policy student, consistently outperforms unconditional successful full-path distillation.

本文在低 prefill 延迟约束下优化 Pareto 前沿以最大化精度，提出 CoinRAG（Contextualized Information Nugget KV Cache Reuse for Long-Context RAG），通过 chunk 级上下文无缝拼接其切片化的 KV 表示。This paper optimizes the Pareto frontier under low prefill latency constraints while maximizing accuracy by proposing CoinRAG (Contextualized Information Nugget KV Cache Reuse for Long-Context RAG), which seamlessly assembles their sliced KV representations with a chunk-level context.

本文提出 Exact Adaptive Hybrid Retrieval（EAHR），将完整列表加权 RRF 定义的有序 Top-K 固定为检索目标，并将通道深度视为请求特定的执行状态，在全部 150 组 query-snapshot 组合中复现了完整列表的有序 Top-20。This work proposes Exact Adaptive Hybrid Retrieval (EAHR), which fixes the ordered Top-$K$ defined by complete-list weighted RRF as the retrieval target and treats channel depth as request-specific execution state and reproduced the complete-list ordered Top-20 in all 150 query-snapshot combinations.

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.

本文通过深度多模态嵌入视觉与自然语言数据，提出了一种用于图文双向检索的模型，并引入结构化的最大间隔目标，使该模型能够显式地跨模态关联片段。This work introduces a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data and introduces a structured max-margin objective that allows this model to explicitly associate fragments across modalities.

论文证明，使用标注数据进行微调，并允许模型查询外部知识源，能够在安全性和事实性这两个关键挑战上带来显著提升。It is demonstrated that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

本文利用源自 CNN 的同一基础信息重新审视初始搜索与重排序两个检索阶段，显著改进了现有基于 CNN 的识别流水线。This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN, and significantly improves existing CNN-based recognition pipeline.

通过引入扩散卷积运算，本文展示了如何从图结构数据中学习基于扩散的表示，并将其作为节点分类的有效基础。Through the introduction of a diffusion-convolution operation, it is shown how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification.