1686 张论文卡片 · OA 绿色
在输入和输出直接进行 4 倍下采样与上采样的设定下，实验表明，基于纯 Transformer 的 U 形编码器-解码器网络优于完全卷积或 Transformer 与卷积相结合的方法。Under the direct down-sampling and up-sampled of the inputs and outputs by 4x, experiments demonstrate that the pure Transformer-based U-shaped Encoder-Decoder network outperforms those methods with full Convolution or the combination of transformer and convolution.
BLIP 通过引导式 caption 方式有效利用含噪网络数据，由 captioner 生成合成 caption，并由 filter 去除噪声样本；在以零样本方式直接迁移到视频-语言任务时，展现出强大的泛化能力。BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones, and demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner.
本文提出了首个用于睡眠阶段分类的深度学习方法，无需计算频谱图或提取手工特征即可端到端学习，利用了全部多变量多模态 PSG 信号（EEG、EMG、EOG），并能利用每个 30 秒窗口数据的时序上下文。This work introduces here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data.
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.
提出一种用于符号音乐的分层自监督"世界模型"，采用 2.55M 参数的 Swin V2 编码器，在 MIDI 钢琴卷帘图像上以 JEPA 风格目标（音高与时间平移等变性、掩码嵌入预测以及分布正则化）训练，无需标签与乐理词汇。A hierarchical self-supervised ``world model'' for symbolic music is presented, using a 2.55M-parameter Swin V2 encoder trained on MIDI piano-roll images with JEPA-style objectives (pitch- and time-shift equivariance, masked embedding prediction, and a distributional regularizer), using no labels and no music-theory vocabulary.
本综述旨在厘清自进化 coding agent 的概念边界，为设计更具适应性、可靠性与软件感知能力的 agentic 系统奠定基础。This survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.
这些结果将视觉地理定位确立为场景文本仲裁的连续诊断手段，并提供了一个受控框架，用于评估 MLLMs 如何解决冲突的多模态证据。These results establish visual geolocation as a continuous diagnostic of scene-text arbitration and provide a controlled framework for evaluating how MLLMs resolve conflicting multimodal evidence.
一个持久化执行状态、使运行可被中断、能在崩溃后存活并继续的框架，必须为已经触发的副作用界定"恢复"的含义。五种广泛部署的 Agent 工作流框架给出了不同答案，且均未公开可机器验证的契约，其行为甚至违背了它们自己声明的片段。RESUME CONTRACT 针对持久化 API 陈述了六项性质（前缀延续、副作用恰好一次、分支确定性、检查点有效性、消费一次、恢复确定性），并附加分支意图与活性义务。TLA+ 模型对参考语义进行了检验……A framework that persists execution state so a run can be interrupted, survive a crash, and continue must decide what a resume means for effects that already fired. Five widely deployed agent workflow frameworks answer differently, none exposes a machine-checkable contract, and behavior violates even the fragments they state. The RESUME CONTRACT states six properties over the persistence API (prefix continuation, effect exactly-once, fork determinism, checkpoint validity, consume-once, recovery determinism), plus fork-intent and liveness obligations. A TLA+ model checks a reference semantics e
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.
FinanceHarness 是一个运行金融工具与从业者引导工作流的框架，端到端自动化金融深度研究：环境与数据构建、Agent 执行循环以及奖励建模；FinanceGym 包含论点驱动的研报问题与评分标准，结合 pre-cutoff 与 post-cutoff 准则。FinanceHarness is presented, a harness that runs finance-oriented tools and practitioner-guided workflows, automating financial deep research end to end: environment and data construction, the agent execution loop, and reward modeling, and FinanceGym, comprising thesis-driven research questions and rubrics that combine pre-cutoff and post-cutoff criteria.
EnvACE 是一种 Agentic 强化学习方法，用 world rehearsal 替代训练中的外部环境交互，将 world rehearsal 确立为突破外部环境约束、扩展 LLM Agent 训练的新路径。EnvACE is introduced, an agentic reinforcement learning method that replaces external environment interaction during training with world rehearsal, establishing world rehearsal as a new path toward scaling LLM agent training beyond the constraints of external environments.
CalibForge 是一个面向终端任务的自动合成系统，利用已验证的求解器行为，通过对抗式求解器校准来修订候选任务；消融实验表明，两种策略都比仅靠人工撰写加验证、或普通单求解器反馈产生更有效的监督信号。CalibForge is presented, an autonomous terminal-task synthesis system that uses verified solver behavior to revise candidate tasks through adversarial solver calibration and ablations show that both strategies yield more effective supervision than authoring and validation alone or ordinary single-solver feedback.
PaDoc 是一个布局驱动的解析器，将预测布局视为共享页面表示上的分支结构，在五个并发级别下均为最快的 end-to-end 解析器。PaDoc is proposed, a layout-grounded parser that treats the predicted layout as a branching structure over a shared page representation that is the fastest end-to-end parser at five concurrency levels and is the fastest end-to-end parser at five concurrency levels.
EffectLearner 是一个语义推理增强框架，结合基于 VLM 的 Object-Effect Reasoner 与基于 DiT 的 Video Eraser，在 EffectWorld-Eval 和具有挑战性的 EffectWorld-Wild 上均取得明显优势，证明其能在复杂真实场景中实现高质量的视频物体擦除。EffectLearner is proposed, a semantic-reasoning-enhanced framework that combines a VLM-based Object-Effect Reasoner with a DiT-based Video Eraser that achieves clear advantages on both EffectWorld-Eval and the challenging EffectWorld-Wild, demonstrating its ability to deliver high-quality video object removal in complex real-world scenes.
World-to-Wrist VLA (W2-VLA) 是一个用于细粒度机器人操作的 VLA 模型，具备任务条件下的未来腕部建模；W2-CoT 是一个合成流水线，生成描述操作进度、物理过渡线索和腕部局部证据的结构化标注。World-to-Wrist VLA (W2-VLA), a VLA model for fine-grained robot manipulation with task-conditioned future wrist modeling, and W2-CoT, a synthesis pipeline that produces structured annotations describing manipulation progress, physical transition cues, and wrist-local evidence.
SmartMage 是一个统一的 MLLM，动态调度异构模态以实现语义感知的 3D 场景理解，在五个 3D 场景理解基准上达到 SOTA，并在仅 RGB 视频理解基准上取得具有竞争力的结果。SmartMage is proposed, a unified MLLM that dynamically orchestrates heterogeneous modalities for semantic-aware 3D scene understanding and achieves state-of-the-art performance across five 3D scene understanding benchmarks, and attains competitive results on RGB-only video understanding benchmarks.
识别出像素级嵌入的不可见元数据痕迹，表明大规模语义监督（无论是类别标签还是亿级规模的 caption）在预训练中会自然引发元数据-语义相关性，导致模型将低层信号转化为预测特征。Invisible metadata traces embedded at the pixel level are identified, suggesting that large-scale semantic supervision, whether through categorical labels or billion-scale captions, naturally induces metadata-semantics correlations during pretraining, leading models to convert low-level signals into predictive features.
提出 OSReward，一个面向真实场景的高质量基准，用于评估 VLM 评判器在 CUA 轨迹上的表现；同时发布一个面向 CUA 社区的、带推理标注的开放轨迹判断语料库，以弥补大规模可靠 CUA 奖励的缺口。OSReward is introduced, a realistic, high-quality benchmark that evaluates VLM judges on CUA trajectories, and an open corpus of reasoning-annotated trajectory judgments for the CUA community, to close the gap in reliable CUA reward at scale.
两个维度之间的取舍在于：操作者事先能承诺的内容越少，所得到的保证就越不确定——极端情况下就只能求助于人工。The trade-off across both planes is that the less an operator can commit to in advance, the less deterministic the resulting guarantee, down to asking a human.
提出构建经济世界模型（作为生成式引擎）的实施路线图：异质 Agent 在其中行动、交互、适应并与市场和制度共同演化，由此从内部生成经济动态。This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic dynamics from the inside.
MameLoshnLM 是首个专为意第绪语构建的 8B 参数开源语言模型，既为意第绪语 NLP 提供基础，也为历史悠久但数字化程度不足的语言建模开发提供可复用的实践模板。MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish, is presented, providing both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.
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.
提出 DataSpace，一个基准，用于评估数据 Agent 在任务本地异构工作空间中生成可验证表格结果的能力，并指出提升数据 Agent 可靠性的关键挑战。DataSpace, a benchmark in which data agents produce verifiable tabular results from task-local heterogeneous workspaces, is introduced and key challenges for improving data-agent reliability are identified.
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.
在客观与主观指标上的重建和生成结果匹配或超越前沿开源 tokenizer，包括 Wan-2.2、HunyuanVideo-1.5、FLUX.2、MovieGen、StableAudio 和 MMAudio 的 VAE。It is demonstrated that reconstruction and generation results on objective and subjective metrics matches or surpasses frontier opensource tokenizers, such as VAEs from Wan-2.2, HunyuanVideo-1.5, FLUX.2, MovieGen, StableAudio and MMAudio.