论文

提出 Multi-Head Latent Control，一种轻量级层，读取冻结 LLM 或 VLM 的隐状态轨迹以生成部署时的控制信号，从而支持从部分生成的提前交接，并在多模型系统中实现更准确的干预决策。Multi-Head Latent Control is introduced, a lightweight layer that reads hidden-state trajectories from a frozen LLM or VLM to produce deployment-time control signals, enabling early handoff from partial generations and more accurate intervention decisions in multi-model systems.

研究表明，当冻结模型与外部记忆配合、且该记忆将每个 episode 提炼为可检索的自然语言规则时，反馈信号足以支撑持续学习。It is shown that feedback is a sufficient signal for continual learning when the frozen model is paired with an external memory that distils each episode into retrievable natural-language rules when the frozen model is paired with an external memory.

实验跟踪器能够展示训练的进展，但更改正在运行的训练通常仍需编写特定于训练器的代码。我们提出了 Interactive Training 2，一个通过共享协议引导训练的开源控制平面。训练应用声明其暴露的设置和操作，人类与自动化控制器通过同一接口提交请求，训练循环在安全的控制点对请求进行验证并应用。定制的 Aim 工作区将实时指标与控制面板以及请求和结果的时序记录整合在一起。我们在 a[……]上展示了该系统。Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system a

已部署的平台与其面向运维的评估共同构成了一条可信赖、统计上可靠的 AI 辅助工作流，适用于设施运维，并可推广到其他大型科学仪器。Together, the deployed platform and its operations-grounded evaluation present a promising workflow for trustworthy, statistically grounded AI assistance in facility operations, transferable to other large scientific instruments.

结果表明，RAG 增强的 LLM 能显著提升生成文本的事实一致性、领域专属性与规范精度，同时降低产生无支持内容的风险；本地部署的 RAG 增强 LLM 不应仅被视为文本生成工具，而应作为认知计算基础设施中的语义处理模块，在法律和信息高度动态的环境中支撑合规与组织决策。The results demonstrate that augmenting LLMs with RAG significantly improves the factual consistency, domain specificity and normative precision of generated texts while reducing the risk of unsupported content generation and indicate that locally deployed LLMs enhanced with RAG should be regarded not merely as text generation tools but as semantic processing modules within cognitive computing infrastructures supporting regulatory compliance and organizational decision-making in environments characterized by high legal and informational volatility.

大量实验表明，结合端上延迟感知设计与定制化训练策略，建立了高效 LVLM 编码的新 SOTA，在端上以近 1.7 倍速度运行的同时显著优于现有以编码器为中心的基线。Extensive experiments demonstrate that the on-device latency-informed design combined with the tailored training strategy establishes a new state-of-the-art for efficient LVLM encoding, significantly outperforming existing encoder-centric baselines while operating on-device at nearly 1.7xthe speed.

本文提出问题：与其缩减真实机器人数据占比，不如提高无机器人 UMI 数据的保真度，从而在后训练阶段移除该 anchor；并开源 HiFi-UMI，一套面向轨迹精度、夹爪间相对位姿、同步与视场协同设计的便携式 UMI 数据生产系统。It is asked whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot fraction, can remove that anchor at post-training, and open-source HiFi-UMI, a portable UMI data-production system co-designed for trajectory accuracy, inter-gripper relative pose, synchronization, and field of view.

本文研究无人机（UAV）可见光-近红外（VNIR）高光谱图像中 PFM-1 地雷的检测，使用光谱角制图（SAM）、匹配滤波器（MF）、自适应相干估计器（ACE）和约束能量最小化（CEM）。This paper studies PFM-1 landmine detection in unmanned aerial vehicle (UAV) visible and near-infrared (VNIR) HSI using spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM).

将文件系统的默认设置转化为 agent memory 的设计空间，证明模型并非塑造 store 形态的唯一杠杆：仅调整工具集即可以与更换模型相当的力度重塑 store。The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.

提出统一框架，融合时频图结构学习与协变量感知的表示融合，证实其在建模选择性变量交互、利用协变量提升预测精度方面的有效性。This work proposes a unified framework integrating time–frequency graph structure learning with covariate-aware representation fusion, confirming its effectiveness in modeling selective variable interactions and leveraging covariates for improved forecasting accuracy.

Meshy T2在几何保真度上达到SOTA，端到端图像到网格生成中位耗时6秒，比自回归基线快一个数量级以上。Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds, over an order of magnitude faster than autoregressive baselines.

结果表明，DEFRAG 缩小了 SLM 与 LLM 之间的精度差距，同时相比集中式服务，成本降低高达 98.4%，峰值吞吐量提升高达 97.8%，证明了 DEFRAG 在边缘实现 LLM 服务普惠化的潜力。Results show that DEFRAG narrows the SLM–LLM accuracy gap, while reducing cost by up to 98.4% and increasing peak throughput by up to 97.8% over centralized services, demonstrating the potential of DEFRAG for democratized LLM services at the edge.

TEngineDB-V通过将全局段解耦索引物化为关系表，使向量搜索成为Tencent OLAP引擎的一等分析原语，消除scatter-gather执行、降低放大效应，并支持原生存储优化。TEngineDB-V makes vector search a first-class analytical primitive in Tencent's OLAP engine through a global segment-decoupled index materialized as relational tables, eliminating scatter-gather execution, reducing amplification, and enabling native storage optimizations.

一种模型无关的模态失败框架，仅依赖部署可观察信号，返回逐样本失败分类、逐模态互补矩阵（将错误归因到模态）以及响亮 vs 静默 dropout 画像（区分可监控失败与远离决策边界未被标记的失败）A model-agnostic modality-failure framework that returns a per-example failure taxonomy, a per-modality complementarity matrix that attributes error to modalities, and a loud-vs-silent dropout profile separating monitorable failures from those that pass unflagged far from the decision boundary, using only deployment-observable signals.

在 BridgeVLA 基础上开发 BridgeVLA++，引入统一的时空记忆架构，建模持久化的空间上下文与时间交互历史，使其可在保留 BridgeVLA 数据效率与泛化能力的同时对观测历史进行推理。BridgeVLA++ is developed by equipping BridgeVLA with a unified spatio-temporal memory architecture that models persistent spatial context and temporal interaction history that can reason over observation histories while preserving BridgeVLA's data efficiency and generalization capabilities.

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.

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.

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.

将一个确定性、无模型的流水线编译进 Agent 记忆：该流水线将本地采集流切分为类型化的活动帧与有界事件片段，携带应用、站点、时间、输入量以及回指原始行的证据指针，全程无模型参与。A deterministic, zero-model pipeline is compiled into agent memory with a deterministic, zero-model pipeline that segments a local capture stream into typed activity frames, bounded episodes carrying application, site, timing, input volume, and evidence pointers back to the raw rows, with no model in the loop.

提出 YOLO-PEFT，一个结构感知的框架，将 adapter 的放置建模为可审计的约束规划问题，以显式、可审查的规划取代手工对目标模块的试错，同时保留已验证的 train-save-merge-export 路径。YOLO-PEFT is proposed, a structure-aware framework that formulates adapter placement as an auditable constraint-planning problem that replaces manual target-module trial and error with explicit, inspectable planning while preserving verified train-save-merge-export paths.

往返一致性将可逆性转化为生成式模型一种实用的可信信号；双向训练带来负成本，在两个方向上均优于单向专家模型；其中反向还可作为快速的逆问题求解器。Round-trip consistency turns reversibility into a practical trust signal for generative models, and Bidirectional training comes at negative cost, beating direction specialists in both directions, and the backward direction doubles as a fast inverse solver.

本文提出一种结构化协议，通过对开源 LLM 评估与安全工具的分类驱动分析来自动化 AI 风险缓解，并给出一个可同时适用于开源与商用方案的分类驱动框架。This paper proposes a structured protocol to automate AI risk mitigation through a taxonomy-driven analysis of open-source LLM evaluation and security tools, and presents a taxonomy-driven framework applicable to open-source and proprietary solutions.

本文是一项关于如何提升蒸馏训练效率的实践研究，围绕两项系统贡献展开，并提出一种融合的 chunked KL loss，使峰值内存随序列长度线性增长。A practitioner's study of how to make distillation training efficient is presented, organised around two systems contributions, and a fused, chunked KL loss is introduced, making peak memory linear in the sequence length.

本文在每个时间步以骨骼作为输入，引入一种新的正则化方案来学习骨骼关节的共现特征，并提出一种同时作用于 LSTM 神经元门、单元和输出响应的新型 dropout 算法。This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.

提出 Decoupling CLI Agent Scaffolding（DCAS），一种后端替换的拦截层，可在不修改 scaffold 的前提下，在任意 CLI scaffold 与任意后端模型之间路由 API 流量，从而支持跨 scaffold 评估与具备规划感知的轨迹采集。Decoupling CLI Agent Scaffolding (DCAS) is introduced, a backend-substitution interception layer that routes API traffic between any CLI scaffold and any backend model without modifying the scaffold, enabling cross-scaffold evaluation and planning-aware trajectory collection.

所提架构通过提供模块化、面向实现的具身认知能力 IVA 部署框架，弥合高层智能体推理模型与实时具身执行之间的鸿沟，助力在复杂交互虚拟环境中构建可扩展、自适应且可解释的智能体。The proposed architecture contributes by providing a modular, implementation-oriented framework for the deployment of embodied, cognitive-capable IVAs and bridges the gap between high-level agent reasoning models with real-time embodied execution, for scalable, adaptive, and explainable agents in complex interactive virtual environments.

提出 MultiMedQA 基准，整合六个现有医学问答数据集（涵盖专业医学、研究与消费者查询）及一个全新的在线医学问题搜索数据集，并提出针对模型答案的人工评估框架，揭示了 LLM 在医学领域的潜在应用价值。MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, is presented and a human evaluation framework for model answers is proposed, suggesting the potential utility of LLMs in medicine.

VeriForge 是一种混合主动写作系统，通过划分认知劳动，使系统在领域发现上承担主动权，而作者保留对叙事合成的完全主动权；在受控的冷启动写作任务中，专家评审者认为其产出在领域扎根方面更强。VeriForge is a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.

本文提出 UniMoMo，一种后训练压缩框架，将其形式化为约束图粗化问题，并引入分层自适应保护机制，根据路由暴露度限制对高流量 expert 的合并。UniMoMo, a post-training compression framework formulated as a constrained graph coarsening problem, is introduced, and a layer-adaptive protection mechanism that restricts the merging of high-traffic experts based on their routing exposure is introduced.

本文提出 LiveAnimate，据作者所知是首个将实时流式生成与十亿参数规模下的稳定长视频生成相结合的系统，基于 140 亿参数的视频 Diffusion Transformer（DiT）。This work presents LiveAnimate, to their knowledge the first animation system to combine real-time streaming with stable long-form generation at billion scale, built on a 14B-parameter video Diffusion Transformer (DiT).

SKILLER 是一个由自然语言驱动的强化学习框架，旨在为小模型自动生成执行器特定的 skills，使用强模型作为 actor 和 critic，将小模型 Agent 系统视为环境，并通过自然语言完全传递所有强化学习信号。SKILLER is a natural-language-driven reinforcement learning framework designed to automatically generate executor-specific skills for small models, which employs a strong model as the actor and critic, treats the small-model agent system as the environment, and propagates all reinforcement learning signals entirely via natural language.

本文给出了 LLM routing 的统一形式化，将其刻画为由五个组件构成的序贯决策过程：context 编码器、模型编码器、评分函数、决策规则和学习信号，涵盖单轮、多轮和个性化 routing。This work presents a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing.

本文将 S2G-RAG 的结构化充分性-缺口判断适配到冻结的 Search-R1 流程中，并在来自 900 个不相交 HotpotQA 问题的 3009 个状态上训练了一个 Qwen3.5-2B judge，以减少检索次数同时广泛保持答案准确性。This work adapts S2G-RAG's structured sufficiency-and-gap judgment to a frozen Search-R1 pipeline and trains a Qwen3.5-2B judge on 3,009 states from 900 disjoint HotpotQA questions to reduce retrieval while broadly preserving answer accuracy.

在多个 benchmark 数据集上的实证评估表明，RAEF 在准确率和推理开销方面均优于 RAF，并且与零样本及微调基础模型的全面对比显示，RAEF 在避免微计算负担的同时取得了与微调相当或更优的性能。Empirical evaluation across multiple benchmark datasets demonstrates that RAEF outperforms RAF in both accuracy and inference overhead, and comprehensive comparisons with zero-shot and fine-tuned foundation models show that RAEF achieves competitive or superior performance to fine-tuning while avoiding its computational burden.

提出一种 chunked-prefill 策略，可缓解由此带来的内存容量惩罚，在 32 GiB 共享内存上将允许的上下文宽度扩展 $2.7 \times$，但即便降低了内存开销，仍需打补丁才能使 Nanbeige4.2-3B 可用。A chunked-prefill strategy is introduced which alleviates the incurred memory-capacity penalty, extending allowable context width by $2.7 \times$ on 32~GiB shared memory, however, even with the reduced memory overhead, it is shown that patches are required to render Nanbeige4.2-3B usable.