1086 张论文卡片
针对一系列基本增广与任意具有平稳统计量的图像数据集，以解析方式根据对比损失计算最优表示，结果表明对于某些增广，最优解可由第一层滤波器为正弦函数的 CNN 实现。Analytically computing the optimal representation in terms of a contrastive loss for a range of basic augmentations and any image dataset with stationary statistics shows that for certain augmentations the optimum can be attained by a CNN whose first layer filters are sinusoids.
提出 SpectraReward，一种无需训练的将预训练 MLLM 转化为即用型奖励模型的奖励函数，用于图像生成强化学习；并引入 Self-SpectraReward，这是统一多模态模型的一种特例，其中策略自身的理解分支充当其生成分支的奖励模型。SpectraReward is proposed, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning, and Self-SpectraReward is introduced, a special case for unified multimodal models where the policy's own understanding branch serves as the reward model for its generation branch.
基于 LLM 的编程 Agent 显著推动了自动化软件问题解决，但由于对仓库理解不足，仍易出现事实性错误。近期方法尝试通过修复前仓库探索来缓解此问题；然而，其修复驱动策略在未识别 Agent 知识缺口的情况下探索仓库，往往产生不精确的上下文，无法弥补潜在的理解不足。本文提出 ACQUIRE，一种面向软件问题解决的 QA 驱动框架，模拟经验丰富的开发者LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understanding. Recent methods attempt to mitigate this limitation through pre-repair repository exploration; however, their fix-driven strategies explore repositories without identifying the agent's knowledge gaps, often yielding imprecise context that fails to bridge the underlying understanding deficit. In this paper, we propose ACQUIRE, a QA-driven framework for software issue resolution. Mirroring how experienced developer
介绍 AMID，一种面向医学影像模型开发的自主多 Agent 框架，其性能优于所评估的通用 MLE 系统，并在异构任务上接近或匹配强大的人工设计挑战赛方案。AMID is introduced, an autonomous multi-agent framework for medical imaging model development that outperformed evaluated general-purpose MLE systems and approached or matched strong human-designed challenge solutions across heterogeneous tasks.
开发了自动化评分流水线，用于评估多种模型，包括开源权重模型、闭源语言模型、视觉语言模型和图像生成模型，结果显示没有单一模型在所有任务类型上占优，且某些任务对所有评估模型仍具挑战性。An automated grading pipeline is developed to evaluate a wide range of models, including open-weight and closed-source language, vision-language, and image-generation models, and shows that no single model dominates across all task types, and that some tasks remain challenging for all evaluated models.
介绍其潜在原因的理论基础，阐明强化学习算法的渐近性能在性能排名与数据规模之间不存在单调关系。The theoretical foundations of the underlying causes outlining that the asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes are introduced.
除领域内增益外，mid-training 还能缓解 agentic post-training 对非 Agent 编程及非编程工具调用基准（tau-bench、BFCL）造成的能力侵蚀：尽管 mid-training 语料仅含 Python 代码，函数调用的归纳偏置在 post-training 后依然保留，带来稳定的增益。Beyond in-domain gains, mid-training mitigates the capability erosion that agentic post-training otherwise inflicts on non-agent coding and non-coding tool-use benchmarks (tau-bench, BFCL): although the mid-training corpus contains Python code only, the function-call inductive bias survives post-training and yields consistent gains.
提出 EvoGraph-R1，一个自演化 GraphRAG 框架，将知识图谱重新概念化为由 Agent 交互塑造的动态环境，将自演化知识图谱确立为跨模态的基础范式。EvoGraph-R1 is introduced, a self-evolving GraphRAG framework that reconceptualizes knowledge graphs as dynamic environments shaped through agent interactions, establishing self-evolving knowledge graphs as a fundamental paradigm across modalities.
提出 LakeQuest，一个经人工校验的基准，包含 9,846 个 QA 对，用于在真实数据湖场景下评估端到端检索与综合 pipeline，并揭示现代问答系统中的关键失效模式。LakeQuest is introduced, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes and exposes critical failure modes in modern QA systems.
尽管视觉-语言模型（VLMs）已取得成功，但误导性图表因欺骗性视觉结构与失真数据表示仍构成重大挑战。我们提出 ChartCynics，一个通过"怀疑式"推理范式揭露视觉欺骗的 Agentic 双路径框架。与整体化模型不同，ChartCynics 将感知与验证解耦：诊断式视觉路径通过策略性 ROI 裁剪捕获结构异常（如倒置坐标轴），OCR 驱动数据路径确保数值根植性。为解决跨模态冲突，我们提出Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations. We present ChartCynics, an agentic dual-path framework designed to unmask visual deception via a "skeptical" reasoning paradigm. Unlike holistic models, ChartCynics decouples perception from verification: a Diagnostic Vision Path captures structural anomalies (e.g., inverted axes) through strategic ROI cropping, while an OCR-Driven Data Path ensures numerical grounding. To resolve cross-modal conflicts, we introduce
提出 SDABench，一个围绕六项能力（描述性、探索性、推断性、推断性、预测性、因果性、机制性）跨五大领域（生物、化学、环境、地理、物理）重新组织评估的基准。SDABench is introduced, a benchmark that reorganizes evaluation around six capabilities (descriptive, exploratory, inferential, inferential, predictive, causal, and mechanistic) across five domains (Biology, Chemistry, Environment, Geography, Physics).
MAGIC 是一个四阶段 pipeline，能将单一自然语言提示转化为可运行的多场景游戏项目，相比 LLM 基线与 Holodeck 可恢复更多真实 portal，并生成显著更可导航的布局。MAGIC is a four-stage pipeline that turns a single natural-language prompt into a runnable multi-scene game project that recovers more ground-truth portals and yields markedly more navigable layouts than an LLM baseline and Holodeck.
视觉语言模型（VLMs）在 DocVQA、ChartQA、MMLongBench-Doc 等视觉文档理解基准上表现强劲。但真实文档融合长度、布局复杂度、模态、问题难度等多因素，难以将模型失败归因于具体原因。我们提出 SynthDocBench，一个全合成的长上下文视觉文档理解基准，系统性控制文档长度、布局结构、模态构成与问题类型等因子。该基准的构建……Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficult to attribute model failures to specific causes. We introduce SynthDocBench, a fully synthetic benchmark for long-context visual document understanding that systematically controls factors including document length, layout structure, modality composition, and question type. The benchmark is constr
本研究将朴素搜索的根因追溯到生成器特有的、可演化的知识边界——即生成器经训练可内化的内容与必须保留于外部上下文的内容之间的鸿沟，并表明该边界可通过"先教后搜"协同训练框架被有效发现。This work traces the root cause of naive search to a generator-specific, evolving knowledge boundary: the divide between what a generator can internalize through training and what must remain in external context, and shows that it is discoverable through a teach-then-search co-training framework.
本研究考察了 GitHub 项目在各自引入首个 bot 前后两年间的情况，发现变化集中在采纳时点附近，而非逐渐累积，这与一种特定解读一致：可预测、基于规则的 Agent 能够成为社区社交基础设施的一部分。This work examines GitHub projects for two years before and after each adopted its first bot, finding changes cluster around adoption rather than accumulating gradually, consistent with a specific interpretation: predictable, rule-based agents can become part of a community's social infrastructure.
GigaWorld-Policy-0.5 在保留未来视觉动力学训练收益的同时提升了机器人控制的推理效率，并引入 Mixture-of-Transformers 架构，将视觉动力学建模与动作生成分离到专门的专家模块中。GigaWorld-Policy-0.5 preserves the training benefits of future visual dynamics while improving inference efficiency for robot control, and introduces a Mixture-of-Transformers architecture that separates visual dynamics modeling and action generation into specialized experts.
Hallo4D 提出"生成-检测-修正"范式，利用大型多模态语言模型（LMMs）从多视角与多帧渲染中识别并归纳时空不一致性，为一致性感知的内容生成提供了一种可扩展且可泛化的方案。Hallo4D introduces a generation-detection-correction paradigm that leverages large multimodal language models (LMMs) to identify and summarize spatial and temporal inconsistencies from multi-view and multi-frame renderings, providing a scalable and generalizable solution for consistency-aware content generation.
研究表明，通过更强的多模态编码器、Agentic prompt 改写及相关技术来增强 Boogu-Image 系统的理解能力，并结合数据质量、训练流程和 Agentic 推理时扩展的改进，即使在计算预算极为受限的条件下，也能显著提升生成与编辑性能。It is demonstrated that strengthening the understanding capability of the Boogu-Image system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation and editing performance even under highly constrained compute budgets.
论文提出 Vinci2，一个主动式的第一人称视频协助系统，将端侧助手 Vinci 由被动响应推进到主动协助；以及免训练、记忆增强的 Agent EgoMemo，维护三种互补的记忆表征：多尺度时间摘要、语义知识图谱与视觉嵌入档案。Vinci2 is presented, a proactive egocentric assistance system that advances the on-device assistant Vinci from reactive response toward proactivity and EgoMemo, a training-free, memory-augmented agent that maintains three complementary memory representations: multi-scale temporal summaries, a semantic knowledge graph, and visual embedding archives.
论文提出 OAT，将该问题建模为基于神经受控微分方程的单类学习，在潜空间中刻画成功轨迹的动力学模式；实验表明其比基于 prompt 的基线更快，并在领域内和分布外数据集上均稳定优于基线。OAT is proposed, which casts this problem as one-class learning with neural controlled differential equations, modeling the dynamical pattern of successful trajectories in latent space, and is shown to be faster than prompting-based baselines and consistently outperforms them in both in-domain and out-of-distribution datasets.
论文提出 PolicyShiftGuard，一个紧凑的策略条件护栏，采用结合随机策略 SFT（RP-SFT）与边界对策略适配（BP-Adapt）的两阶段训练方案，并验证匹配的通过/拒绝边界对是稳定策略适配的关键。This work proposes PolicyShiftGuard, a compact policy-conditioned guardrail trained with a two-stage training recipe that combines Randomized Policy SFT (RP-SFT) with Boundary-Pair Policy Adaptation (BP-Adapt), and confirms that matched pass/block boundary pairs are essential for stable policy adaptation.
STRACE（Structural TRajectory Analysis and Causal Extraction）是一个用于构建高信噪比优化上下文的框架，旨在对长周期 Agent 实施更精确、更有效的优化。STRACE (Structural TRajectory Analysis and Causal Extraction) is a framework that constructs high signal-noise optimization contexts for more precise and effective optimization of long-horizon agents.
AgentCompass 被提出，它是一个开源、轻量且可扩展的面向 LLM-based Agent 的评估基础设施，将评估流程围绕三个独立组件组织，从而在不重新实现复杂执行逻辑的前提下支持灵活配置。AgentCompass is introduced, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents that organizes the evaluation process around three independent components, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic.
PalmClaw 是一个开源 Agent 框架，原生运行于手机端，直接在设备上管理 session、memory、Skill、工具以及 agent loop，使 Agent 能够直接调用移动端能力，同时保证每一步操作的显式与可控。PalmClaw is an open-source agent framework that runs natively on mobile phones and manages the sessions, memory, skills, tools, and agent loop directly on the device, allowing agents to use mobile capabilities directly while keeping each action explicit and controlled.
本综述将现代具备自我改进能力的 Agent 视为将经验转化为持续能力增益的自适应系统，并提出一个系统级框架，将现代 Agent 建模为由基础模型与由 prompt、memory、工具及控制逻辑构成的运行支撑层相耦合的配置。This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains, and offers a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic.
本文提出一种实用评估协议，将评估重点从任务完成转向经过验证的漏洞发现，可在涵盖多种攻击面与漏洞类型的足够复杂目标上开展评估，并结合结构化真值标注与基于 LLM 的语义匹配来识别漏洞。This paper presents a practical evaluation protocol that shifts assessment from task completion to validated vulnerability discovery, allowing evaluation in sufficiently complex targets spanning multiple attack surfaces and vulnerability classes, and combines structured ground-truth with LLM-based semantic matching to identify vulnerabilities.
本研究表明 DiT 与 ViT 在一个关键方面存在差异：DiT 不会出现 patch-token 异常值，但仍能受益于 registers；并且 registers 在像素空间 DiT 中比在潜空间 DiT 中效果更显著。This work shows that DiTs differ from ViTs in a key respect: they do not exhibit patch-token outliers but still benefit from registers, and finds that registers are more effective in pixel-space DiTs than in latent-space DiTs.
本文提出 Earthquaker-AI，一个混合式教育框架，在已有教育机器人项目基础上集成基于 RAG 的对话式 AI 助手，旨在提升小学生的地震应急准备与主动行动意识。该系统将曾获奖的 STEM 项目 Earthquaker 从 Lego WeDo2 的机械模拟拓展至认知与元认知层面：机器人组件利用 Lego WeDo2 自动化模拟地震响应，使学生能够与传感器和执行器进行交互。This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation. It aims to enhance earthquake preparedness and conscious action among primary-school students. The system extends the award-winning STEM project Earthquaker moving from mechanical simulation with Lego WeDo2 to cognitive and metacognitive processing. The robotics component uses Lego WeDo2 automation to simulate seismic response, letting students interact with sensors and actuat
本文提出 SIS-Bench，一个在统一 self-in-space 表述下评估 UAV 场景具身空间智能的基准，并探索了一种融合光流与视觉特征的运动感知表征，以纳入与自身相关的动态信息。SIS-Bench is introduced, a benchmark for evaluating embodied spatial intelligence in UAV scenarios under a unified self-in-space formulation, and a motion-aware representation that incorporates self-related dynamics through optical flow and visual feature fusion is explored.
面向第 11 届 ABAW 挑战赛的多任务学习系统，在标准确定性架构基础上扩展条件 Rectified Flow 头，建模真实场景下面部行为固有的模糊性，借助蒙特卡洛采样实现不确定性感知的一对多预测。A multi-task learning system for the 11th ABAW challenge that extends a standard deterministic architecture with a conditional rectified-flow head to model the inherent ambiguity of in-the-wild facial behavior, enabling uncertainty-aware one-to-many predictions through Monte Carlo sampling.
压缩可减少推理 token 并保持大部分选择题准确率，同时提示影响接近基线——在一个前沿条件下，降低推理成本移除的证据比单纯缩短轨迹所预期的更多。Compression reduces reasoning tokens and preserves most multiple choice accuracy, while hint influence remains near baseline, in a frontier where reducing reasoning costs removes more evidence than shorter traces alone would predict.
SPEAR 是一个 Python 库，可通过模块化插件架构连接任意 Unreal Engine 应用并对其进行编程化控制；同时引入一种表达力强的高层编程模型，使用户能够以任意数据依赖关系指定复杂的 UE 工作图，并在单个 UE 帧内确定性执行这些图。SPEAR is a Python library that can connect to, and programmatically control, any Unreal Engine application via a modular plugin architecture, and introduces an expressive high-level programming model that enables users to specify complex graphs of UE work with arbitrary data dependencies among work items, and to execute these graphs deterministically within a single UE frame.
本文提出 VideoChat3，一个完全开放、高效且以视频为中心的通用 MLLM，仅以 4B 参数与更高效率，超越参数量相当或更大的已有开源模型。This work introduces VideoChat3, a fully open, efficient, and generalist video-centric MLLM, which surpasses prior open-source models with equal or larger parameter counts with only 4B parameters and higher efficiency.
研究表明模型具备相关子群体知识，但难以稳定传递到聚合估计中，这一差距使统计自一致性成为评估 LLM 的尚未饱和、无需参考的准则。It is suggested that models possess relevant subpopulation knowledge but do not reliably propagate it into aggregate estimates, and this gap establishes statistical self-consistency as an unsaturated, reference-free criterion for evaluating LLMs.
本文将关键帧执行拆解为存在性、保真度、时序顺序、定位、持续性与唯一性六个互补指标，并通过结合专用感知模型的、基于证据的 MLLM 判断来评估整体视频质量。This work decomposes keyframe execution into six complementary metrics covering presence, fidelity, temporal ordering, localization, persistence, and uniqueness, while assessing overall video quality through evidence-grounded MLLM judgments augmented with specialized perception models.
本文提出 MultiRef-Compass，一个面向 MR2AV 生成的统一基准，将自动指标与引入复判增强的 MLLM-as-a-Judge 框架相结合，实现对感知保真度与参考条件合成能力的可扩展、可审计评估。MultiRef-Compass is introduced, a unified benchmark for MR2AV generation that integrates automatic metrics with a rejudging-enhanced MLLM-as-a-Judge framework, enabling scalable and auditable evaluation of both perceptual fidelity and reference-conditioned composition.