论文

实验表明，OpenComputer 的硬编码验证器比 LLM-as-judge 评估更贴合人类裁定，尤其当任务成败取决于细粒度应用状态时。Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state.

RTP-LLM 是一个面向工业级 LLM 部署的高性能推理引擎，已在 Alibaba Group 成功部署，服务超过 1 亿用户，通过集成设计解决根本性瓶颈。RTP-LLM is presented, a high-performance inference engine for industrial-scale LLM deployment, successfully deployed across Alibaba Group serving over 100 million users, and addresses fundamental bottlenecks through integrated design.

总体而言，分阶段检索的影响往往超过"理想证据存在"本身；本文为专业科学场景下 RAG 系统的部署与诊断提供了实践指导，并为构建更可靠、可控的迭代式检索-推理框架奠定了基础。This is the first controlled, mechanism-level diagnostic evaluation of whether synchronized iterative retrieval and reasoning can surpass even an idealized static upper bound (Gold Context) RAG, and practical guidance for deploying and diagnosing RAG in specialized scientific settings.

本文提出三种协议级原语以填补Model Context Protocol的空白：身份传递、自适应工具预算与结构化错误语义，并提出Structured Error Recovery Framework (SERF)，提供机器可读的失败语义以支持确定性的Agent自校正。Three protocol-level primitives are proposed to fill gaps in the Model Context Protocol: identity propagation, adaptive tool budgeting, and structured error semantics, and the Structured Error Recovery Framework (SERF), which provides machine-readable failure semantics that enable deterministic agent self-correction.

研究表明矛盾解析本质上是写入时并发控制，并将缺失的契约——一个在隔离性、模式与来源维度上被证明正确的写入时正确性规范——显式化，固定了每个生产启发式都默认假设、却没有任何已部署系统显式给出的保证。It is shown that contradiction resolution is write-time concurrency control and make the missing contract explicit, a write-time correctness specification, proved sound across isolation, schema, and provenance, pinning the guarantee every production heuristic assumes but no deployed system makes explicit.

本文提出通过让 evaluator 与解决方案协同进化来自动化 evaluator 的设计，并证明突破 evaluation 瓶颈可释放 ADRS 的潜力，为下一代数据系统生成高度优化、可部署的代码。This work proposes automating the design of evaluators by co-evolving them with the solutions, demonstrating that addressing the evaluation bottleneck unlocks the potential of ADRS to generate highly optimized, deployable code for next-generation data systems.

本文探讨 multi-agent system，并指出当前尚未被充分解决的问题，同时探索了 multi-agent system 在区块链系统中的潜在应用，为其在真实分布式系统中的未来发展与落地提供启示。This paper explores multi-agent systems and identifies challenges that remain inadequately addressed, and explores potential applications of multi-agent systems in blockchain systems to shed light on their future development and application in real-world distributed systems.

得益于涵盖全身自由度的扩展预训练数据，LingBot-VLA-2.0 在两个机器人平台上展现出强大的跨具身长时程移动操作能力。Benefiting from the expanded pretraining data that covers whole-body degrees of freedom, LingBot-VLA-2.0 demonstrates strong cross-embodiment long-horizon mobile manipulation capability across the two robotic platforms.

本工作提出 MultAttnAttrib，一种免训练的归因生成方法，利用模型的预填充过程、选定的注意力头以及校准阈值在文档中定位源证据，且在多种归因生成方法上一致地表现更优。This work introduces MultAttnAttrib, a training-free attribution-generation method that leverages a model's prefill pass, selected attention heads, and calibrated thresholds to locate source evidence within a document, and consistently outperforms a variety of attribution-generation methods.

在资源受限环境中，专业癫痫专家稀缺，使基于 LLM 的决策支持对管理纵向治疗的一线临床医生具有吸引力。此类系统必须适应当地处方实践并知道何时转诊。我们在乌干达儿科癫痫诊疗中研究该问题，基于纵向非结构化门诊记录预测抗癫痫用药方案。标准提示与医生处方取得了一定程度的一致性，但神经科医生审查显示许多错误反映的是分布失校的处方默认值而非失败。Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice and know when to defer. We study this problem in Ugandan pediatric epilepsy care, predicting anti-seizure medication regimens from longitudinal unstructured clinic notes. Standard prompting achieves non-trivial agreement with physician prescriptions, but neurologist review shows that many errors reflect distribution-miscalibrated prescribing defaults rather than fail

京东 Oxygen AI 商品中心（Oxygen AIIC）：基于 LLM/VLM 的工业级商品知识生产与服务平台，已在大规模场景下取得可量化的收益。The JD Oxygen AI Item Center (Oxygen AIIC), an industrial-scale platform built on LLMs/VLMs for item-knowledge production and service, has delivered measurable gains at scale.

应在真实类别分布下使用跨网络评估来判断部署就绪度，而非仅依赖域内准确率。Deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone, to suggest deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone.

实验结果表明，所得参数集可生成可区分的个性化换道行为，同时 RAG 始终提升偏好理解效果，尤其对隐式指令效果显著，表明将基于 LLM 的自然语言交互与 Apollo 集成以支持个性化换道行为生成具有潜力。Experimental results show that the derived parameter sets generate distinguishable personalized lane-change behaviors, while RAG consistently improves preference interpretation, particularly for implicit commands, indicating the potential of integrating LLM-based natural-language interaction with Apollo to support personalized lane-change behavior generation.

本文提出 DuoMem，一种双空间蒸馏框架，可将程序化问题求解能力从大型教师模型迁移到紧凑学生模型，并适用于实时边缘部署，而这一点对教师模型而言颇具挑战。DuoMem is introduced, a dual-space distillation framework that transfers procedural problem-solving ability from a large teacher model to compact student models and is viable for real-time edge deployment, which would be challenging for the teacher.

提出 Transparent Two-Pass Execution，一种在推理时将工具执行与 schema 约束响应生成解耦的策略；实验结果表明该方法无需模型重新训练即可恢复工具调用能力，同时保持结构化输出保证。Transparent Two-Pass Execution is proposed, an inference-time strategy that decouples tool execution from schema-constrained response generation and experimental results show that this approach restores tool invocation while preserving structured output guarantees without requiring model retraining.

AOHP 的核心设计原则是将 Agent 视为 OS 中的一等公民，从而支持自适应用户界面以及对 Agent 友好的运行时环境；在任务完成度、执行成本和安全策略合规性方面均展现出明显优势。The core design principle of AOHP is to treat agents as first-class OS actors, enabling adaptive user interfaces and agent-friendly runtime environments, and shows clear advantages in task completion, execution cost, and security-policy compliance.

本研究收集了使用主流 AI Agent 开发的大量真实应用语料，并设计了结合 Agent 辅助代码审计与人工验证的漏洞分析框架，揭示出与传统软件开发流程中常见的漏洞模式不同的反复出现的漏洞规律。This study collects a large corpus of real-world applications developed using popular AI agents and designs a vulnerability analysis framework that combines agent-assisted code auditing with human validation, and reveals recurring vulnerability patterns that differ from those commonly observed in conventional software development workflows.

本文命名了"检索状态锁定"这一失败模式，通过分离单一置信度分数所混淆的三个对象——答案表面、检索到的证据以及检索状态本身——来诊断该问题，并直接衡量"一致性盲区"。This work names the failure retrieval-state lock-in and diagnose it by separating the three objects a single confidence score conflates: the answer surface, the retrieved evidence, and the retrieval state itself, and measures the agreement blind spot directly.

提出 Vesta，一个统一的具身通用模型，将定位、空间推理、导航和长程规划能力整合到单个基础模型中，并证明通用模型能够达到或超越专家模型。Vesta is presented, a unified embodied generalist that consolidates localization, spatial reasoning, navigation, navigation, and long-horizon planning capabilities into a single foundation model and demonstrates that a generalist model can match or exceed specialists.

提出一种基于物体中心的残差强化学习框架，利用物体位姿精化 VLA 动作，使观测空间紧凑，在仿真与现实之间能够一致迁移。An object-centric residual RL framework is proposed that refines VLA actions using object poses, enabling a compact observation space that transfers consistently between simulation and reality.

提出一种多 Agent 框架，通过以确定性编排约束替代 "LLM-as-a-judge" 路由，解决可能在到达患者前未被发现的过早诊断交接与静默临床幻觉问题；观察到 OLDCARTS 完整度与语义熵之间存在统计显著的负相关，提示结构化信息采集与诊断不确定性降低相关。A multi-agent framework that addresses premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient by replacing ``LLM-as-a-judge''routing with deterministic orchestration constraints is proposed and observes a statistically significant negative correlation between OLDCARTS completeness and semantic entropy, suggesting that structured information gathering is associated with reduced diagnostic uncertainty.

结果表明，由 LLM 编排的多 Agent 系统可将传统 AutoML 扩展为可信、自适应且面向生产的 BDaaS 生命周期自动化。The results suggest that LLM-orchestrated multi-agent systems can extend conventional AutoML toward trustworthy, adaptive, and production-oriented BDaaS lifecycle automation.

本文引入 trace-economic underwriting，将工具调用 trace 映射为客户风险敞口与可索赔损失，并以此表示用于定价、控制与风险转移，使用确定性经济标签而非 LLM 评判器。T trace-economic underwriting is introduced, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer, and uses deterministic economic labels rather than an LLM judge.

CoRe（Context Relevance）被提出。该系统在一个大型短视频搜索引擎中每周重新部署，持续运行超过五个月，使用已部署的多模态相关性模型作为源，并以镜像生产融合代数的乘性比值形式来缩小仿真与生产之间的差距。CoRe (Context Relevance) is presented, such a system, redeployed weekly for over five months in a major short-video search engine, using the deployed multimodal relevance model as its source and a multiplicative ratio form mirroring the production fusion algebra to close the simulation-production gap.

FRAMe 展示了先进 LLM 如何被部署用于以人为本的任务规划，将自然语言指令转化为安全、高效且灵活的飞行路线。FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural language instructions into safe, efficient, and flexible flight routes.

该紧凑且量化原生、带内置控制的运行时为物联网音频场景下的端侧语义音频提供了实用基础；通过对转向接口的案例分析，可生成在部分属性上具有真实但有界控制的、承载口味联想的音乐。A compact, quantized runtime with built-in control a practical basis for on-device semantic audio in Internet-of-Sounds settings and a case study of the steering interface generates music carrying taste associations with genuine but bounded control for a subset of attributes.

提出 AgentKGV，一种用于知识图谱事实核查的智能体 LLM-RAG 框架，集成动态路由与迭代查询改写，以应对文档级检索中的表层形式不匹配问题。AgentKGV, the Agentic LLM-RAG framework for KG fact Verification, is proposed, that integrates dynamic routing and iterative query rewriting, which handles surface-form mismatch in document-level retrieval.

Motion4Motion 对视频中角色的 motion flow（而非骨骼）进行建模，使跨物种运动迁移更加容易。Motion4Motionmodels the motion flow of the character in a video instead of skeletons, which makes motion transfer across species easier, which makes motion transfer across species easier.

Xiaomi-Robotics-U0 是首个支持跨多种机器人本体的高质量多视角场景生成、并引入结构化、可控的具身迁移以实现细粒度编辑的模型，同时保持多视角一致性与交互动态。Xiaomi-Robotics-U0 is the first model to support high-quality multi-view scene generation across multiple robot embodiments and to introduce structured, controllable embodied transfer for fine-grained editing while preserving multi-view consistency and interaction dynamics.

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.

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.

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.

本文提出 LongStraw，一个面向目标、感知架构的系统，用于 resident-state 虚拟化、response replay 和分布式梯度执行，它将实时训练图限制在 response 后缀范围内，同时在完整的 GRPO 组内复用代价高昂的 prompt 计算。This work presents LongStraw, an objective-aware, architecture-aware system for resident-state virtualization, response replay, and distributed-gradient execution that bounds the live training graph by the response suffix while reusing the expensive prompt computation across the complete GRPO group.

子空间对齐重连（SAR）表明，从参数几何中提取对推理有效的更新，可作为一种无需训练的机制来提升推理与多领域性能。Subspace-Aligned Rewiring (SAR) shows that extracting reasoning-effective updates from parameter geometry can serve as a training-free mechanism to improve reasoning and multi-domain performance.