论文

提出 CaSKG，一种反事实因果 Skill 图谱框架，在检索前校准程序关系，将边置信度校准定位为大规模紧凑且可执行的 Skill 检索的有效路径。CaSKG, a counterfactual-causal skill graph framework that calibrates procedural relations before retrieval, is proposed, position edge-confidence calibration as an effective route to compact and executable skill retrieval at scale.

本文提出 GameWAM，据其所知是首个面向原生闭环游戏与 GUI 控制的 WAM，并发现 Low-Frequency Action Source Imprinting (LASI)：在固定条件下，采样动作源的低频分量系统性地引导生成的粗粒度相机运动，揭示了生成式控制中的源敏感性失效模式。This work introduces GameWAM, to its knowledge the first WAM for native closed-loop gameplay and GUI control and uncovers Low-Frequency Action Source Imprinting (LASI), in which low-frequency components of the sampled action source systematically steer coarse generated camera motion under fixed conditioning, revealing a source-sensitivity failure mode in generative control.

更大的模型显著更具样本效率，因此最优的算力高效训练方式是：在相对适中的数据量上训练非常大的模型，并在远未收敛时显著提前停止训练。Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

实验结果表明，CritICL 持续优于标准 in-context learning，并以显著更少的生成次数和更低的 token 成本，取得与 test-time scaling 方法相当或更优的性能。Experimental results show that CritICL consistently outperforms standard in-context learning and achieves performance competitive with or superior to test-time scaling methods, while requiring significantly fewer generations and lower token cost.

传统视频编辑主要关注场景级内容，而直播更强调人物主体。然而，直接将现有视频编辑方法应用于以人为中心的直播仍具挑战，因为它们可能引入面部表情不一致，且通常依赖多个离线推理步骤，难以满足实时交互需求。我们提出 EditaLive，一个用于实时流式角色视频编辑的新型框架。具体而言，我们从预训练图像动画模型（Wan-Animate）出发，该模型天然解耦...Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically depend on multiple offline inference steps, making them unsuitable for real-time interaction. We propose EditaLive, a novel framework for real-time streaming character video editing. In detail, we start from a pretrained image animation model (Wan-Animate), which naturally decoupl

提出 TacForcing，一种融合执行期触觉反馈的流式动作生成框架：按顺序生成动作块并保留未完成块的中间状态，并引入执行感知触觉注意力（EATA），将每次触觉更新的直接访问限制在下一个即将执行的块。TacForcing is introduced, a streaming action-generation framework incorporating execution-time tactile feedback that generates action blocks sequentially while preserving intermediate states of unfinished blocks and introduces Execution-Aware Tactile Attention (EATA), which restricts direct access to each tactile update to the next block scheduled for execution.

提出 Luce，一种三维表示方法，将几何与 PBR 材质统一到体素化的多模态高斯云中，分别使用专用高斯基元表示反照率、金属度-粗糙度与法线，在单图到三维生成任务上达到 SOTA。Luce is proposed, a 3D representation that unifies geometry and PBR materials within a voxelized multimodal Gaussian cloud, using dedicated Gaussian primitives for albedo, metallic-roughness, and surface normals to achieve state-of-the-art single-image-to-3D generation.

提出 LayerRecall，一种由当前状态条件化、按层选择的 memory router，仅从历史 K/V state 中检索相关状态并注入到 backbone 特定的 memory-sensitive 层中，其余层保留局部 attention；并提出 Cross-Horizon Prediction Matching (CHPM)，借助特权的长上下文参考在预测空间中对有界 memory router 进行监督。This work introduces LayerRecall, a current-conditioned, layer-selective memory router that retrieves relevant historical K/V states and injects them only into backbone-specific memory-sensitive layers while preserving local attention elsewhere, and proposes Cross-Horizon Prediction Matching (CHPM), which uses a privileged long-context reference to supervise the bounded-memory router in prediction space.

本文提出 Judge co-adaptation from Zero data (J-Zero)，一个统一的 Challenger–Solver–Judge 自进化框架，支持在可验证与不可验证领域的自我提升，并识别出 Judge 协同进化是这一持续改进的关键驱动力。This work proposes Judge co-adaptation from Zero data (J-Zero), a unified Challenger--Solver--Judge self-evolution framework that supports self-improvement across both verifiable and unverifiable domains and identifies Judge co-adaptation as the key driver of this sustained improvement.

提出 Ring Forcing，一个自回归视频扩散框架，旨在稳健构建并精确利用长期记忆，并通过稀疏 RoPE 机制实现灵活、可扩展的记忆适配，同时充分利用预训练先验。Ring Forcing is presented, an autoregressive video diffusion framework designed to robustly construct and precisely utilize long-term memory and a sparse RoPE mechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors.

本工作将 embedding 缩放作为正交于稀疏度缩放的强有力维度加以探索，并推出 LongCat-Flash-Lite，一个从零训练的 68.5B 参数、约 30 亿激活参数的模型，不仅超越参数等量级的 MoE 基线，还对同规模现有模型展现出卓越竞争力。This work explores embedding scaling as a potent, orthogonal dimension for scaling sparsity and introduces LongCat-Flash-Lite, a 68.5B parameter model with ~3B activated trained from scratch that not only surpasses parameter-equivalent MoE baselines but also exhibits exceptional competitiveness against existing models of comparable scale.

提出 StepGuard，一种 step-level guard model，可对已完成的 agent trajectory 进行审计并在工具动作执行前进行检查；并引入 StepGen，一种自动数据引擎，能在风险步生成上下文相同但动作不同的安全与不安全 trajectory。This work proposes StepGuard, a step-level guard model that can audit completed agent trajectories and check tool actions before they are executed, and introduces StepGen, an automatic data engine that generates safe and unsafe trajectories with the same context but different actions at the risky step.

StarHarness 提供了一种实用方法，通过根据基线失败行为对任务进行分层、将 proposer 可见的搜索任务与 proposer 隐藏的选择任务分离，并为评估泛化能力保留 held-out 任务，从而缓解工具密集型企业任务中持续的 model-environment mismatch。StarHarness offers a practical way to reduce persistent model-environment mismatch in tool-rich enterprise tasks by stratifying tasks according to baseline failure behavior, separating proposer-visible search tasks from proposer-hidden selection tasks, and reserves held-out tasks for evaluating generalization.

该框架将循环序列模型中的 temporal alignment、plasticity、forgetting 与 bounded rehearsal 分离，并结合数值稳定的 positive-decay renormalization，使其在语言建模上保持竞争力，同时提升在可变位数加法任务上的长度外推能力。This framework separates temporal alignment, plasticity, forgetting, and bounded rehearsal in recurrent sequence models, together with numerically stable positive-decay renormalization, to remain competitive in language modeling and improve length extrapolation on variable-digit addition.

GGSS——Geodesic-Gated Spherical Steering——一种保范的干预方法，在单位超球面上发现反事实偏差子空间，沿 geodesic arc 引导视觉 token，并通过自适应门控聚焦于承载更强人口统计信号的 token。GGSS---Geodesic-Gated Spherical Steering---a norm-preserving intervention that discovers a counterfactual bias subspace on the unit hypersphere, steers visual tokens along geodesic arcs, and uses an adaptive gate to focus correction on tokens that carry stronger demographic signal.

本文提出 Cross-lingual Ranking Preference Optimization (CRPO)，一种新框架，利用来自英语的鲁棒偏好知识来促进目标语言的偏好对齐，从而增强语言适应性与输出质量。This paper proposes Cross-lingual Ranking Preference Optimization~ (CRPO), a novel framework that leverages robust preference knowledge from English to facilitate preference alignment in the target language, thereby enhancing language adaptation and output quality.

提出 Intention Distillation (INDI)，将行为级意图蒸馏到动作解码器中，并以目标依赖的方式组织下游预测；研究表明，动作解码器显式建模其生成行为的语义目标能够带来收益。Intention Distillation (INDI) is proposed, which distills behavior-level intent into the action decoder and organizes downstream predictions in an objective-dependent manner, and shows that action decoders benefit from explicitly modeling the semantic objective of the behavior they generate.

本文提出 DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation)，一种将范式从全局 forcing 转向拓扑引导的局部修正的新框架，相对传统 full-trajectory 基线取得显著提升。This work proposes DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction, and significantly outperforms traditional full-trajectory baselines.

ViT-5 的设计与当代基础模型实践保持一致，可作为对 vanilla ViT 的直接替换升级方案，适用于 2020 年代中期的视觉骨干网络，并为生成建模提供更强大的骨干。With a design aligned with contemporary foundation-model practices, ViT-5 offers a simple drop-in upgrade over vanilla ViT for mid-2020s vision backbones and serves as a stronger backbone for generative modeling.

该研究将 358,896 条消息切分为 22,329 个时间连贯的块，并构建三种搜索表示：raw_text、生成的 summary，以及将 summary 与 raw_text 片段及其他固定文本相结合的 embedding_text。This study segmented 358,896 messages into 22,329 temporally coherent chunks and constructed three search representations: raw_text, a generated summary, and embedding_text, which combines a summary with a raw-text excerpt and other fixed text.

提出 CamoDocs，一种通过将对抗文档伪装在良性内容中来避免直接包含查询的投毒攻击，并表明 TrustRAG 等以擦除为主的聚类防御可降低 ASR，但会在 NeoQA 等依赖检索的基准上造成显著的效用下降。CamoDocs is proposed, a poisoning attack that avoids direct query inclusion by camouflaging adversarial documents among benign content, and shows that erasure-heavy clustering defenses such as TrustRAG can reduce ASR, but only with substantial utility drops on retrieval-dependent benchmarks such as NeoQA.

该工作表明带 sink 的 Sliding Window Attention（SWA）表现不逊于甚至优于后训练的 Linear Attention 模型，并建议改用 SWA 而非后训练线性模型。This work shows that Sliding Window Attention (SWA) with sinks performs as well or better than post-trained Linear Attention models, and recommends switching to SWA instead of post-training linear models.

结果表明，可靠的 Agent 自我进化需要协同设计验证、状态接地、见证语义和恢复语言表达能力，而非仅依赖迭代提示。The results indicate that reliable agent self-evolution requires co-designing verification, state grounding, witness semantics, and recovery-language expressivity rather than relying on iterative prompting alone.

一种神经符号架构，将逻辑知识图谱（LKG）与动态求解器路由相结合，并引入基于本体的 LKG，将逻辑规则和约束视为一等拓扑节点，从而支持对从文本中抽取的依赖关系进行显式建模。A Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing, and introduces an ontology-based LKG that treats logical rules and constraints as first-class topological nodes, enabling explicit modeling of dependencies extracted from text.

该候选名单可在不牺牲检索质量的前提下短路全语料库加密搜索：200–500 个候选即可在 5 个零样本语料库（规模从 25K 到 5.4M 文档）中与全语料库检索效果接近匹配。This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents.

Pera 描述了一种持久化 Agent，围绕感知和控制组件组织，持续从情景任务执行、上下文及周围环境变化中感知服务相关信号，并利用这些信号构建生命周期任务。Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.

GLM-5 在真实编码任务中展现出前所未有的能力，在端到端软件工程挑战的处理上超越既有基线，并提出了新颖的异步 Agent RL 算法，进一步提升了 RL 质量。GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges and proposing novel asynchronous agent RL algorithms that further improve RL quality.

通过发布紧凑的 7B 生成器和 2K Refiner，该工作致力于使原生音视频生成平民化，并为统一音视频生成建模的未来研究提供可及的基础。By releasing the compact 7B generator and 2K Refiner, this work seeks to democratize native audio-video generation and provide an accessible foundation for future research in unified audio-video generative modeling.

结果表明，在所测试的单调用、预填工具场景下，当偏好信息通过工具传入或需从原始产物中推断时，CoT 监控的可靠性可能下降。The results suggest that CoT monitoring may be less reliable when preference information arrives through tools or must be inferred from raw artifacts, within the single-call, prefilled-tool setting tested here.

该论文提出 SafeAtlas-VL，一个包含 1.5M 训练实例的数据集，将图像、请求和响应级判断置于五级有序量表上，并通过 target-conditioned tuning 训练 SafeAtlas Guard 系列模型，用于多模态安全检测。This paper introduces SafeAtlas-VL, a dataset of 1.5M training instances that places image-, request-, and response-level judgments on a five-level ordered scale, and trains the SafeAtlas Guard series of models via target-conditioned tuning for multimodal safety detection.

提出 Hash-Atlas 网络，将 3D 场景编辑重新表述为对 2D atlas 图像的操作，从而实现 2D 编辑与 3D 重建流程的解耦。The Hash-Atlas network is proposed, which reformulates 3D scene editing as operations on 2D atlas images, thereby achieving a workflow decoupling of the 2D editing and 3D reconstruction processes.

提出 PRISK，一个具备自动化数据生成和定制化指标的动态评估框架，用于揭示当前 LLM 个性化中的系统性局限以及个性化信息如何塑造其响应。PRISK is proposed, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses.

本文利用 LSTM 网络学习视频序列表示，并通过在 UCF-101 与 HMDB-51 数据集上对人体动作识别这一监督学习任务进行微调来评估所学表示。This work uses Long Short Term Memory networks to learn representations of video sequences and evaluates the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.

端到端天气预报系统直接从原始地球观测数据生成高质量的全球网格与站点预报，仅以数值天气预报流水线（含数据同化）一小部分的成本取而代之。这类系统是确定性的，不输出不确定性结果。本文通过在每个组件上附加一种随机机制，将 Aardvark Weather 模型概率化：在观测编码器中加入学习到的、依赖输入的噪声，以捕捉源自观测系统的偶然不确定性；在处理器中使用 Monte Carlo dropout，以捕捉认知不确定性。End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned, input-dependent noise at the observation encoder, capturing aleatoric uncertainty inherited from the observing system, and Monte Carlo dropout in the processor, capturing epistemic

提出 SpanCalib-VLM，这是一种用于 SHROOM-Visions 共享任务的混合双系统，结合了多模态序列标注器（由 XLM-RoBERTa-Large 与 SigLIP 视觉编码器通过交叉注意力融合而成）与微调后的生成式 VLM（Qwen3.5-4B-SHROOM-SFT）。SpanCalib-VLM is presented, a hybrid dual-system for the SHROOM-Visions Shared Task that combines a multimodal sequence tagger, consisting of XLM-RoBERTa-Large fused with a SigLIP vision encoder via cross-attention, with the fine-tuned generative VLM (Qwen3.5-4B-SHROOM-SFT).

基于对数据的深入定性分析和既有理论研究，构建了一套新颖且全面的多模态错误信息分类法，并由此获得了关于社交媒体用户如何在实际场景中将图像与文本结合以传播错误信息的此前未被记录的洞见。A novel, comprehensive taxonomy of multimodal misinformation grounded in an in-depth qualitative analysis of the data and prior theoretical work is developed, which leads to previously undocumented insights about how social media users combine images with text to spread misinformation in the wild.