论文

本综述旨在阐明 PFMs 在可扩展性、安全性、逻辑推理能力、跨领域学习能力以及面向 AGI 的友好交互能力方面的研究。This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence.

本综述对图嵌入文献进行全面回顾，并提出两种图嵌入分类法，分别对应不同图嵌入问题设置中的挑战以及现有工作如何在解决方案中应对这些挑战。This survey conducts a comprehensive review of the literature in graph embedding and proposes two taxonomies ofGraph embedding which correspond to what challenges exist in differentgraph embedding problem settings and how the existing work addresses these challenges in their solutions.

本综述从数据管理视角对数据收集进行全面研究，提供了相关操作的研究全景图、何时使用何种技术的指导原则，并识别出值得关注的研究挑战。This survey performs a comprehensive study of data collection from a data management point of view, providing a research landscape of these operations, guidelines on which technique to use when, and identify interesting research challenges.

提出一个模型，基于图像区域上的 CNN、句子上的双向 RNN 以及通过多模态嵌入对齐两种模态的结构化目标，生成图像及其区域的自然语言描述。A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.

在大规模复杂的移动边缘网络中，涉及具有不同约束的异构设备，这为大规模 FL 实施带来了通信成本、资源分配以及隐私安全方面的挑战。In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.

本综述旨在通过对基本思想与关键原则的系统回顾以及对不同算法与技术的合理分类，提供在线机器学习文献的全面综述。This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques.

提出 Manhattan NMF（MahNMF），通过最小化 $X$ 与 $W^T H$ 之间的 Manhattan 距离建模重尾 Laplacian 噪声，并以迭代方式提升 MahNMF 及其扩展的近似精度。Manhattan NMF (MahNMF) is presented which minimizes the Manhattan distance between $X and $W^T H$ for modeling the heavy tailed Laplacian noise and improves the approximation accuracy iteratively for both MahNMF and its extensions.

结果表明，通过结合基础 LLM 改进（PaLM 2）、医学领域微调以及包括新颖集成精化方法在内的提示策略，医学问答正快速接近医生水平的表现。Results highlight rapid progress towards physician-level performance in medical question answering by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach.

深入回顾文献中 GAN 相关研究，并从两个视角阐述针对三大挑战所提出的架构变体与损失变体。An in-depth review of GAN-related research in the literature is provided, and an account of the architecture-variant and loss-variants, which have been proposed to handle these three challenges from two perspectives are provided.

总结了利用人工智能以及语音与语言处理预测阿尔茨海默病相关认知衰退的现有研究发现。The existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease are summarized.

本文介绍 Gemma，一族基于 Gemini 模型所使用的研究与技术构建的轻量级 SOTA 开源模型，并全面评估模型的安全性与责任性，同时详细描述模型开发过程。This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models, and presents comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development.

提出一种全可微、样本级影响力估计器，可在不进行耗时的逆 Hessian 乘积或凸性假设的前提下，量化因添加或移除数据而产生的参数变化。A fully differentiable, sample-level influence estimator that quantifies parameter shifts from adding or removing data, without time-consuming inverse-Hessian products or convexity assumptions is introduced.

本实证研究通过建模数据组成对计算定律及分配指数的影响，推导出指定模型规模、token 数和数据混合精确配置的效率前沿，为可预测地扩展多模态基础模型奠定了必要基础。This empirical research establishes the essential groundwork for predictably scaling multimodal foundation models by modeling the influence of data composition on compute laws and allocation exponents and derive an efficiency frontier specifying precise configurations of model size, token count, and data mixture.

该工作主张将研究构想视为多目标的联合问题，并将其建模为 Quality-Diversity (QD) 搜索；同时提出 IDEAgent，一个通过 lineage 管理思路演化的 multi-agent 框架。This work argues that research ideation should be treated as a conjunction of both objectives and framed as a Quality-Diversity (QD) search, and introduces IDEAgent, a multi-agent framework that manages the evolution of ideas through lineages.

该工作发布了 LAMAR，一种具备语言感知能力的多语种 cross encoder，在训练中兼顾语义相关性与语言连贯性，在通用多语种 reranking 基准上整体以及各语言单独评估中均达到最佳性能。This work releases LAMAR, a language aware multilingual cross encoder trained to account for both semantic relevance and language coherence, which achieves the best performance overall and across all languages examined individually on general multilingual reranking benchmarks.

近期条件视频生成模型已展现出将 3D 引擎渲染（如深度图与无纹理几何体）转化为照片级真实视频的潜力，可应用于游戏与沉浸式内容创作。此类应用要求长时程自回归生成，在持续合成新帧的同时维持持久的 3D 世界。自回归生成器以有界 KV cache 逐 chunk 合成视频，因此当相机再次访问已从上下文中驱逐的位置时，模型常会重新生成不一致的外观，尽管该Recent conditional video generation models have shown promising potentials to transform 3D engine renderings, such as depth maps and untextured geometry, into photorealistic videos for gaming and immersive content creation. These applications require long-horizon auto-regressive generation that continuously synthesizes new frames while preserving a persistent 3D world. Auto-regressive generators synthesize video chunk by chunk with a bounded KV cache, so when the camera revisits a location after its context has been evicted, the model often regenerates inconsistent appearance, even though the

Molt 是一个 PyTorch-native 的训练框架，旨在压低上述开销：代码库精简清晰，研究者能完整装进脑中，AI 编程助手也能完整阅读和推理，从而可以端到端地追溯和修改算法流程。Molt is a PyTorch-native training framework built to keep that cost small: a codebase compact and clean enough for a researcher to hold in their head, and for an AI coding assistant to read and reason about in its entirety, so the algorithm flow can be traced and changed end to end.

提出 Multi-Head Latent Control，一种轻量级层，读取冻结 LLM 或 VLM 的隐状态轨迹以生成部署时的控制信号，从而支持从部分生成的提前交接，并在多模型系统中实现更准确的干预决策。Multi-Head Latent Control is introduced, a lightweight layer that reads hidden-state trajectories from a frozen LLM or VLM to produce deployment-time control signals, enabling early handoff from partial generations and more accurate intervention decisions in multi-model systems.

VisCo 是一个训练高效的自压缩框架，复用预训练 VLM 本身作为内在压缩器，使用少量 memory token 压缩视觉信息，并将层次化信息从编码传递到解码。VisCo is a training-efficient self-compression framework that reuses the pretrained VLM itself as an intrinsic compressor that compresses visual information using a small set of memory tokens and transfers hierarchical information from encoding to decoding.

研究表明，当冻结模型与外部记忆配合、且该记忆将每个 episode 提炼为可检索的自然语言规则时，反馈信号足以支撑持续学习。It is shown that feedback is a sufficient signal for continual learning when the frozen model is paired with an external memory that distils each episode into retrievable natural-language rules when the frozen model is paired with an external memory.

本研究在 Reachy Mini 机器人平台上，通过系统提示、检索增强生成 (RAG) 和有状态的提示编排，将选定的 KBD 需求落地实现，表明 KBD 可以塑造负责任的机器人行为，并有望提升机器人辅助学习中的学习效果。This study operationalized selected KBD requirements in the Reachy Mini robot platform through system prompting, retrieval-augmented generation, and stateful prompt orchestration, indicating that KBD can shape responsible robot behavior and potentially increase learning effectiveness in robot-supported learning.

提出 Spectral Alignment，一种基于 guidance 的轻量级方法，将中间预测的功率谱校准到预先计算的先验，并与 Classifier-Free Guidance (CFG) 互补。Spectral Alignment is proposed, a lightweight, guidance-based method that calibrates the power spectrum of intermediate predictions to a pre-computed prior and is complementary to Classifier-Free Guidance (CFG).

该工作在多语句、多模态设定下研究 ASV，考察跨匿名语音聚合信息是否影响隐私，并发现帧级聚合得到的 EER 最低。This work investigates ASV in a multi-utterance, multimodal setting and examines whether aggregating information across anonymized speech impacts privacy, and finds that frame-level aggregation yields the lowest EERs.

这些结果将 tracked scattering 确立为通往高维 one-step generation 的路径，并给出一张设计图，将 diffusion 相关监督、Drift-like 动力学与 GAN-like 目标联系起来。These results establish tracked scattering as a route to high-dimensional one-step generation and provide a design map relating diffusion-related supervision, Drift-like dynamics, and GAN-like objectives.

该工作提出一个面向异常检测的免训练 agentic 框架，无需领域特定知识，旨在追踪被检测对象随时间变化的空间-时间动态与底层变换，然后基于逐对象的时间状态轨迹进行推理，在 grounding 帧中识别异常对象。This work introduces a training-free agentic framework for anomaly detection free of domain-specific knowledge, designed to track spatial-temporal dynamics and underlying transformations of detected objects over time, and then reason over the object-wise temporal state trajectories to identify abnormal objects in grounded frames.

实验跟踪器能够展示训练的进展，但更改正在运行的训练通常仍需编写特定于训练器的代码。我们提出了 Interactive Training 2，一个通过共享协议引导训练的开源控制平面。训练应用声明其暴露的设置和操作，人类与自动化控制器通过同一接口提交请求，训练循环在安全的控制点对请求进行验证并应用。定制的 Aim 工作区将实时指标与控制面板以及请求和结果的时序记录整合在一起。我们在 a[……]上展示了该系统。Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system a

提出 DataPrep-Bench，首个统一基准，在共享的下游任务 grounding 协议下，对 LLM 驱动的数据准备在六个领域、多种 base model 上的两类能力进行联合评估。DataPrep-Bench is introduced, the first unified benchmark that jointly evaluates both capabilities under a shared downstream-grounded protocol over six domains and multiple base models of LLM-driven data preparation.

已部署的平台与其面向运维的评估共同构成了一条可信赖、统计上可靠的 AI 辅助工作流，适用于设施运维，并可推广到其他大型科学仪器。Together, the deployed platform and its operations-grounded evaluation present a promising workflow for trustworthy, statistically grounded AI assistance in facility operations, transferable to other large scientific instruments.

DeCoRAG 是一个多模态 Graph RAG pipeline，将知识处理从耦合的视觉-语义推理转向认知层面的 Decoupling，进而把推理空间从稠密、带噪的背景推向纯净、意图驱动的语义簇。DeCoRAG is a multimodal Graph RAG pipeline that shifts knowledge processing from coupled visual-semantic reasoning to cognitive Decoupling, and subsequently drives the reasoning space from dense, noisy backgrounds to purified, intent-driven semantic clusters.

本文提出一个半符号化框架，将用于 OCR 后校正的 word-spotting 技术与知识图谱表示相结合，使 agent 可通过合成查询访问信息，从而对误读和幻觉具有鲁棒性。This paper introduces a semi-symbolic framework that integrates word-spotting techniques for post-OCR correction with a knowledge graph representation that enables the agent to access information through synthesized queries that are robust to misinterpretation and hallucination.

结果表明，RAG 增强的 LLM 能显著提升生成文本的事实一致性、领域专属性与规范精度，同时降低产生无支持内容的风险；本地部署的 RAG 增强 LLM 不应仅被视为文本生成工具，而应作为认知计算基础设施中的语义处理模块，在法律和信息高度动态的环境中支撑合规与组织决策。The results demonstrate that augmenting LLMs with RAG significantly improves the factual consistency, domain specificity and normative precision of generated texts while reducing the risk of unsupported content generation and indicate that locally deployed LLMs enhanced with RAG should be regarded not merely as text generation tools but as semantic processing modules within cognitive computing infrastructures supporting regulatory compliance and organizational decision-making in environments characterized by high legal and informational volatility.

将 Positive--Direction Matching (PDM)——一种分支感知的 OPD 目标，分别约束正预测方向与 CFG 条件方向——引入 dense-to-sparse 视频控制；由于朴素的 guided matching 对推理 guidance 尺度极为敏感，分支感知监督可实现更鲁棒、更有效的知识迁移。Positive--Direction Matching (PDM), a branch-aware OPD objective that separately constrains the positive prediction and the CFG conditional direction, is introduced to dense-to-sparse video control, where naive guided matching is highly sensitive to inference guidance scales, while branch-aware supervision enables more robust and effective knowledge transfer.

提出一个统一且受控的多轮环境，可在三阶段精确控制长程规划；研究表明，多教师 on-policy 蒸馏 (MOPD) 通过收敛到跨环境共享的规划模式来整合多种能力。A unified and controlled multi-turn environment that enables precise control of long-horizon planning across three stages is introduced and it is shown that multi-teacher on-policy distillation (MOPD) integrates capabilities by converging to shared planning-pattern across environments.

本研究考察是否存在一条实用路径，在没有坐标界面、且无需高成本区域级监督的条件下提升归因效果，并指出了这样一条可行路径。A study investigates whether there is a practical path to improve attribution without a coordinate interface and without costly region-level supervision, and indicates a practical path to improve attribution without a coordinate interface and without costly region-level supervision.

提出一种面向历史文档修复的新框架，利用搭载 RAG 的大语言模型，有效缓解了推断上下文相关专有名词的难题。A novel framework for historical document restoration that leverages large language models with retrieval-augmented generation (RAG) and effectively mitigates the challenge of inferring context-dependent proper nouns is introduced.

本文提出无需训练的 Sol-Attn（Sparsifying online attention），在单次 online-softmax pass 中统一动态路由、稀疏计算与近似修正，在稀疏注意力中取得更好的精度–效率权衡。This paper introduces training-free Sol-Attn (Sparsifying online attention), which unifies dynamic routing, sparse computation, and approximation correction in a single online-softmax pass, achieving a better accuracy-efficiency trade-off in sparse attention.