论文

在作者研究的粗粒度属性上，MLLM 编码了视觉证据但无法可靠控制对其的依赖For the coarse attributes the authors study, MLLMs encode the visual evidence but cannot reliably control their reliance on it, indicating that for the coarse attributes they study, MLLMs cannot reliably control their reliance on it.

结果是面向稀疏 event-KV 服务的记忆契约：写入什么、落在何处、源消失后什么得以保留The result is a memory contract for sparse event-KV serving: what to write, where it lands, and what survives once the source is gone.

提出 PAST-Bench 基准，用于隔离评估持久 agent 从经验保留到系统性改进的能力；开发 Hermes+，提升了来自保留经验的平均增益，并提供更清晰的路径证据。The PAST-Bench benchmark is introduced, a benchmark designed to isolate how persistent agents can progress from retaining experience to systematically improving through it, and Hermes+ is developed, which raises the average gain from retained experience and provides clearer pathway evidence.

提出 Video-DR，采用解耦的感知-探索流水线与分阶段工具解锁，强制在 web 检索前进行充分的跨帧视觉定位，实现突破模仿学习上限的自主探索。Video-DR is introduced, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval, enabling autonomous exploration that breaks the imitation-learning ceiling.

提出 AntiSkillBench，一个端到端的基准，用于评估 persona-skill 流水线中的风险与防御；实验表明 persona-skill 风险在不同的 agent backbone 和蒸馏协议下持续存在，从显式属性扩展到沟通风格与个性特征。AntiSkillBench is introduced, an end-to-end benchmark for evaluating risks and defenses across the persona-skill pipeline, and experiments show that persona-skill risks persist across agent backbones and distillation protocols, extending from explicit attributes to communication styles and personality traits.

提出 Knowledge-Geometry Decoupling (KGD) 并引入 Behavioral Multi-Token Prediction (BMTP)，仅将协作或语义相关的未来项作为监督，从而得到更干净、更可迁移的行为知识。Knowledge-Geometry Decoupling (KGD) is proposed and Behavioral Multi-Token Prediction (BMTP) is introduced to retain only collaboratively or semantically related future items as supervision, yielding cleaner and more transferable behavioral knowledge.

将 Agent-Centric Interactive World Proxies 概念化，将基础范式从物理状态转移转向 agent 可用的信息转移，如执行结果、检索到的经验或技能、以及验证信号，扩展了世界建模的范围，为持续改进的 agent 提供多样化反馈。This work conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile feedback for continually improving agents.

高粘度污渍以其高粘度与复杂流变特性，仍是机器人表面清洁的主要挑战。传统擦拭往往扩散污渍，而擦洗摩擦力更强却存在损伤表面的风险。本文提出 Push-Wiper，一种将高粘度污渍清洁重构为聚集问题的框架。Push-Wiper 使用海绵通过分段推送轨迹渐进式聚集污渍，随后通过后处理阶段剥离已聚集物质并实现海绵自清洁。我们采用逐步Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional wiping often spreads the stain, while scrubbing provides stronger friction but risks damaging the surface. In this paper, we propose Push-Wiper, a framework that reformulates viscous stain cleaning as an aggregation problem. Push-Wiper employs a sponge to progressively gather stains through segmented pushing trajectories, followed by a post-processing phase that detaches the aggregated material and enables sponge self-cleaning. We adopt a stepw

提出 Semantic-Temporal WAM (ST-WAM)，使用 DINOv3 作为未来预测与历史检索的共享语义表示，同时保留细粒度 VAE 动力学，以提升动作鲁棒性；证明语义-时间建模能有效补充像素生成动力学，实现稳健的操作。Semantic-Temporal WAM (ST-WAM) is proposed to improve action robustness by using DINOv3 as a shared semantic representation for future prediction and history retrieval while retaining fine-grained VAE dynamics, demonstrating that semantic-temporal modeling effectively complements pixel-generative dynamics for robust manipulation.

提出 ExplainBench，一个自动评估 coding agent 解释的基准，基于信息性解释应能让 LLM 正确回答问题的直觉，实现 agent 之间解释质量的量化比较。This work proposes ExplainBench, a benchmark to automatically evaluate explanations from coding agents, based on the intuition that informative explanations should enable an LLM to correctly answer questions, allowing quantitative comparison of explanation quality between agents.

提出 ContinualSkillBench，一个面向 in-context 持续 skill 学习的动态评估框架，表明当前 in-context skill 进化机制能够支持持续适应，但仍难以稳定地将经验整合为鲁棒且可迁移的 skill。ContinualSkillBench is introduced, a dynamic evaluation framework for in-context continual skill learning that shows that current in-context skill evolution mechanisms can support continual adaptation, but still struggle to consistently consolidate experience into robust and transferable skills.

发现 ALiBi 的失效模式会显著损害 token 检索，而对标准 decoder 基准影响较小；提出四种训练时缓解策略，在 passkey 检索上获得最一致的提升。It is found that ALiBi's failure mode can substantially impair token retrieval while having only a minor effect on standard decoder benchmarks, and proposes four training-time mitigation strategies that yield the most consistent improvements in passkey retrieval.

提出 V2N (Video to Notes)，首个完整的 VPT 系统：共享时序 backbone 驱动 onset、offset、key hold、velocity 等任务专属 head，采用逐帧监督而非仅在窗口中心进行联合训练。This work presents V2N (Video to Notes), the first complete VPT system: a shared temporal backbone feeds task-specific heads for onset, offset, key hold, and velocity, jointly trained with per-frame supervision rather than only at the window center.

PosterMELD 是一个模板条件的多 agent 流水线：capacity-aware slot 在渲染前引导写作，确定性 gate 与 VLM 审核将失败路由到有界修复，在生成的多种方法中获得最高的条件 CHE 并产出多个可印刷输出。PosterMELD is a template-conditioned multi-agent pipeline: capacity-aware slots guide writing before rendering, and deterministic gates plus vision-language model (VLM) review route failures to bounded repair result in the highest conditional CHE among generated methods with multiple print-ready outputs.

STAMPlus 解决了超出单目标预测的三难问题，解耦自回归对话与非自回归 mask 预测，取得 SOTA 分割性能，同时保持通用多模态指令遵循能力，并降低 12 类别延迟。STAMPlus resolves the trilemma beyond single-target prediction, decoupling autoregressive dialogue from non-autoregressive mask prediction and achieves state-of-the-art segmentation performance, preserves general multimodal instruction following, and reduces 12-category latency.

提出 MiniWorld，一个从零训练流式视频世界模型的可复现框架，采用 chunk-wise 非递减噪声调度与两阶段继续训练，提升时间建模与稳定性，将促进未来视频世界模型的研究。MiniWorld is presented, a reproducible framework for training streaming video world models from scratch that adopts a chunk-wise non-decreasing noise schedule and two-stage continued training to improve temporal modeling and stability and will facilitate future research on video world modeling.

为人体动作识别引入新问题域：基于标准 RGB 视频对儿童步态行为进行细粒度分析，并描述一个统一的端到端框架用于解码儿科步态的基本组成。This work introduces a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video, and describes a unified end-to-end framework for decoding fundamental components of pediatric gait.

FinIndices 是一个大规模基准，在未裁剪的财务报表（最长 32K tokens）上评估数据处理保真度，带来显著的零提示增益，验证通过以数据为中心的对齐可部分恢复结构化逻辑。FinIndices is a large-scale benchmark evaluating data-processing fidelity over uncropped financial statements (up to 32K tokens) and yields substantial zero-hint gains, validating that structured logic can be partially restored via data-centric alignment.

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.

简化了 MoE 路由算法，设计出通信与计算成本更低的直观改进模型，并首次证明大型稀疏模型可以使用更低精度格式进行训练This work simplifies the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs and shows large sparse models may be trained, for the first time, with lower precision formats.

Pointer sentinel-LSTM 模型在 Penn Treebank 上以远少于标准 softmax LSTM 的参数量达到 SOTA 语言建模性能，并开源了 WikiText 语料库The pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank while using far fewer parameters than a standard softmax LSTM and the freely available WikiText corpus is introduced.

在视觉问答与视觉定位任务上对多模态紧凑双线性池化（MCB）进行了广泛评测，结果一致表明 MCB 优于去掉 MCB 的消融版本This work extensively evaluates Multimodal Compact Bilinear pooling (MCB) on the visual question answering and grounding tasks and consistently shows the benefit of MCB over ablations without MCB.

DASH (Drift Aware advantage SHaping) 根据每个推理片段是朝向还是偏离正确性来分配片段级 credit，在 overthinking 普遍的场景下取得最高准确率，同时减少 overthinking 行为，比基线实现更高效的自纠错。DASH (Drift Aware advantage SHaping), which assigns segment-level credit based on whether each reasoning segment leads toward or away from correctness, achieves the highest accuracy where overthinking is prevalent while reducing overthinking behaviors and achieving more productive self-correction than baselines.

贡献一个由欧洲联盟法院 (CJEU) 判决构建的大规模法律 IR 数据集，支持在多个 query-document 层级上开发与严格评估法律 IR 方法。This work contributes a large-scale legal IR dataset constructed from Court of Justice of the European Union (CJEU) judgments, which supports both the development and rigorous evaluation of legal IR methods, at multiple query-document levels.

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.

提出 ChronoLens，结合冻结的多语言语言模型、特征对齐的 crosscoder 与事后语言学干预，应用于来自五个议会传统、跨越 1803–2026 年的 4498 万篇文档和约 172 亿 tokens，表明历史语言变化是一个结构化的、多维度的过程。ChronoLens is introduced, a framework that combines frozen multilingual language models, feature-aligned crosscoders, and post-hoc linguistic interventions, and applies it to 44.98 million documents and approximately 17.2 billion tokens from five parliamentary traditions spanning 1803--2026, showing that historical language change is a structured, multidimensional process.

提出 ARCHead，一种紧凑型 LM-head 压缩器，结合量化低秩核心、组级 INT4 残差以及基于激活导出度量拟合的低秩校正，在 block 量化器无法触及的大输出投影上实现互补压缩。ARCHead is presented, a packed LM-head compressor that combines a quantized low-rank core, group-wise INT4 residuals, and a low-rank correction fitted in an activation-derived metric to complements block quantizers by compressing the large output projection they can leave untouched.

提出 RestoreKV，在相同 KV 总预算下以可学习的恢复机制补充基于选择的 KV 压缩方案，显著降低压缩带来的性能损失。RestoreKV is introduced, which complements this selection-based formulation with learned restoration with learned restoration under the same total KV budget, and substantially reduces compression-induced degradation.

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.

研究表明在希腊语专业语料上，无参数 BM25 基线优于多个现成的多语言稠密检索模型，并推出首个大规模希腊语 RAG 基准 HERA，同时发布适配模型与基准以支撑未来希腊语 RAG 系统研究。This study shows that a parameter-free BM25 baseline outperforms several off-the-shelf multilingual dense retrieval models on specialist Greek corpora and introduces HERA, the first large-scale Greek benchmark for retrieval-augmented generation, and releases adapted models and benchmark to support future research on Greek-language RAG systems.

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.