论文

JEPA-TTT 在测试时间内持续适配预训练动作条件 Joint-Embedding Predictive Architecture 世界模型的潜在动力学预测器，平均将自回归潜在预测误差降低 83%，并将规划性能相较冻结的 JEPA 世界模型提升 153%。JEPA-TTT, which adapts the latent dynamics predictor of a pretrained action-conditioned Joint-Embedding Predictive Architecture world model throughout test time, reduces autoregressive latent prediction error by 83% on average and improves planning performance by 153% over the frozen JEPA world model.

为每个可查询的条件分布推导出一个尾部影响度量，聚合其不确定性对每一次 Bellman 复用中 CVaR 的影响，并刻画了尾部最优与均值最优分配重合的精确网格（exact-grid）机制。A tail influence for each queryable conditional law that aggregates how its uncertainty affects CVaR across every Bellman reuse is derived, and an exact-grid regime in which tail- and mean-optimal allocations coincide is characterized.

提出 WM-VLM，为预训练 VLM 配备一个轻量级世界模型分支以生成中间视觉状态，表明内部世界模型为 VLM 在语言与视觉双重空间中的推理提供了一条有前景的路径。WM-VLM is introduced, which equips a pretrained VLM with a lightweight world model branch for generating intermediate visual states and suggests that internal world models offer a promising path toward VLMs that reason in both language and visual space.

提出 Nexus，一个将每次调用视为持久任务的云-边平台，结果表明局部性、操作作用域权限与持久结果标识如何支持具有工作负载相关成本的云-边 Agent 服务。Nexus, a cloud-edge platform that treats each invocation as a persistent task, is presented and results demonstrate how locality, operation-scoped authority, and persistent result identity support cloud-edge agent services with workload-dependent costs.

提出 ACG-Bench，一个面向逐臂组合泛化（arm-wise Compositional Generalization）的基准，为超越固定训练流程的双臂策略设计提供实证指导，考察了 arm-token grouping、技能专属 LoRA 适配器（SkillLoRA）以及 arm-wise attention（AWA），凸显了技能条件化参数与注意力结构的互补性。This work introduces ACG-Bench, a benchmark for arm-wise Compositional Generalization that provides empirical guidance for designing dual-arm policies that generalize beyond fixed training routines, and examines arm-token grouping, skill-specific LoRA adapters (SkillLoRA), and arm-wise attention (AWA), highlighting the complementarity of skill-conditioned parameters and attention structure.

提出 LaMem-VLA，一种以潜在记忆为核心框架的方法，将历史经验重建为潜在记忆 token，并直接与 VLA 推理交织，使记忆能够在有界上下文下直接参与 VLA 推理并引导动作生成。LaMem-VLA is introduced, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning, and enables memory to directly participate in VLA reasoning and guide action generation under a bounded context.

提出 MemAdapter，一种自适应整合检索记忆以支持客观、可靠推理的新框架，证明 MemAdapter 在多种场景下持续提升记忆可靠性。This work proposes MemAdapter, a novel framework that adaptively integrates retrieved memories to support objective and reliable reasoning and demonstrates that MemAdapter consistently improves memory reliability across diverse scenarios.

实验表明，相较于编码 Agent 直接生成游戏世界以及现有基线方法，Code2Games 一致性地提升了生成游戏世界的视觉质量、交互保真度，以及经引擎适配后游戏成品的质量。Experiments demonstrate that, compared with direct gaming-world generation by coding agents and existing baseline methods, Code2Games consistently improves the visual quality and interactive fidelity of generated gaming worlds, as well as the quality of the resulting games after engine adaptation.

Perturbot 和 GroundingFscore 提供了一套训练与评估框架，用于将 VLA 决策与捷径先验解耦，同时保持对任务相关证据的响应性。Perturbot and GroundingFscore provide a training-and-evaluation framework for disentangling VLA decisions from shortcut priors while preserving responsiveness to task-relevant evidence.

引入 Mathematical Primitive 概念来探查结构性数学理解，并提出 \hlei{}，一个沿发现（Discovery）、生成（Generation）、消化（Digestion）和执行（Execution）四个维度评估数学推理的新型基准。This paper introduces the notion of Mathematical Primitive to probe structural mathematical understanding and proposes \hlei{}, a novel benchmark that evaluates mathematical reasoning along four distinct dimensions: Discovery, Generation, Digestion, and Execution.

提出 SourceLearn，结合两种互补的学习机制以捕获源的可复用理解，包括其知识的结构、解读与应用方式，并以一个捕获源可复用理解的持久 source model 来表示该能力。This work proposes SourceLearn, which combines two complementary learning mechanisms that captures reusable understanding of the source, including how its knowledge is structured, interpreted, and applied, and represents this competence with a persistent source model that captures reusable understanding of the source.

提出一种"失败即关闭"的临时可见性协议：在截止时间前接纳新内容，隐藏验证未及时提交的项目，并支持按谱系范围的遏制，同时量化新鲜度与可用性之间的权衡。A fail-closed provisional-visibility protocol is presented that admits new content under a deadline, hides items whose verification has not committed in time, and supports lineage-scoped containment, and quantifies the freshness and availability trade-offs.

提出基于 Agentic AI 的零信任架构（Agentic-ZTA），通过协调的多 Agent 决策流水线将 NIST SP 800-207 ZTA 架构的控制循环落地，并证明使用 AI agent 执行零信任的可行性。This paper presents Agentic AI based zero trust architecture (Agentic-ZTA) that operationalizes the NIST SP 800-207 ZTA architecture control loop through coordinated multi- agent decision pipeline and demonstrates the feasibility of enforcing zero trust using AI agents.

提出 Behavior-Preserving KV Cache Compression，一种无需训练的框架，通过估计候选条目被移除后压缩缓存所诱导的 logits，并利用驱逐前的前向统计量评估其与完整缓存下一 token 分布之间的 KL 散度来为候选驱逐打分。This work proposes Behavior-Preserving KV Cache Compression, a training-free framework that scores candidate evictions by estimating the compressed-cache logits induced by their removal and evaluating the resulting KL to the full-cache next-token distribution using pre-eviction forward statistics.

DynaKRAG 是一种统一的证据-动作框架,通过学习共享的状态条件策略来协调上述操作,证明统一的状态条件证据控制能在保证强回答质量的同时,实现高效检索与紧凑的答案生成上下文。DynaKRAG, a unified evidence-action framework that learns a shared state-conditioned policy for coordinating these operations, demonstrates that unified, state-conditioned evidence control supports strong answer quality, efficient retrieval, and compact answer-generation contexts.

结果表明，基于查询的循环记忆组合能够提升长上下文建模能力，并在超出训练上下文范围之后依然有效，同时每个 chunk 仅使用紧凑的仿射摘要。Results demonstrate that query-dependent composition of recurrent memory improves long-context modeling and remains effective beyond the training context while using compact per-chunk affine summaries.

提出基于流的自潜在推理（Flow-based Latent Reasoning, FLaRe），一份简洁方案，涵盖潜空间编码内容及其塑形方式、流训练位置、答案读取方式，以及最终在模型自身已验证思考上进行训练的阶段。Flow-based Latent Reasoning (FLaRe), a simple recipe covering what the latent space encodes and how to shape it, where to train the flow, how to read out the answer, and a final stage of training on the model's own verified thoughts, is presented.

提出 UndoBench，一个覆盖 8 个企业领域、36 个基础工作流与 36 个故障场景的基准测试，通过相同种子下的反事实配对试验以及链路级效应历史与环境状态预言机，将任务能力与恢复能力解耦。UndoBench is introduced, a benchmark spanning 36 base workflows and 36 fault scenarios across 8 enterprise domains, decoupling task competence from recovery capability via counterfactual paired trials under identical seeds alongside wire-level effect-history and environment-state oracles.

综述了包括广泛使用的"恐惧诉求"在内的说服技巧的适用性，并提炼出意识宣传活动的基本要素以及导致活动成败的关键因素。The suitability of persuasion techniques, including the widely used 'fear appeals', are reviewed, and essential components for an awareness campaign as well as factors which can lead to a campaign's success or failure are extracted.

表格基础模型通过以标注训练样本为条件进行上下文学习 (ICL) 来预测。与分离训练与推理的传统模型不同，这些模型必须在每次前向传播中处理所有训练样本，导致每次预测成本高昂。限制训练样本数量可降低成本，但会显著降低性能。我们不丢弃上下文，而是提出激活对齐 (activation alignment)，利用完整上下文来教会模型在仅看到子集时如何行为。这是通过训练一个……Tabular foundation models perform in-context learning (ICL) by conditioning predictions on labeled training examples provided as context. Unlike traditional models that separate training from inference, these models must process all training examples in every forward pass, making each prediction expensive. Restricting the number of training examples reduces this cost but substantially degrades performance. Instead of discarding context, we propose activation alignment, a method that leverages the full context to teach a model how to behave when seeing only a subset. This is achieved by trainin

提出 LLM-as-Jev，一种保留架构的框架，直接从带括号数字标识符的下一 token 概率中提取校准决策，并同时给出无需训练的推理方案与一种微调目标——通过树分解的列表式损失优化候选选择，并使用 KL 散度惩罚将辅助预测锚定到基模型。This work presents LLM-as-Jev, an architecture-preserving framework that extracts calibrated decisions directly from next-token probabilities over bracketed numeric identifiers, and provides both a training-free inference recipe and a fine-tuning objective that optimizes candidate selection via a tree-factorized listwise loss while anchoring auxiliary predictions to the base model using KL divergence penalties.

将 Clalit Health Services 涵盖超过 540 万患者的专家组中所蕴含的证据提炼为一个已发布的评分工具，支持隐私保护的生物标志物假设生成，并将 Agent 的"提出—评分—精化"循环锚定于真实世界数据，同时不暴露任何患者数据。This work distilled the evidence held in the Clalit Health Services panel of over 5.4 million patients into a released scoring tool, which supports privacy-preserving biomarker hypothesis generation and grounds an agent's propose-score-refine loop in real-world data without exposing any patient data.

这是一篇综述文章，围绕机器学习技术（特别是大语言模型）展开，旨在以对数值方法领域专家友好的方式阐释 LLM 的核心概念。This is a survey article that centers on machine learning techniques, with a particular focus on large language models, to clarify the core concepts behind Large Language Models in a manner accessible to specialists in numerical methods.

一项预注册研究，基于留出的 LoCoMo 对话与 LongMemEval 数据，发现 Jev 以 LLM reranker 三分之一的延迟实现了同等的选取准确率，并且优于多轮 graph traversal 调用。A pre-registered study on held-out LoCoMo conversations and LongMemEval finds Jev selects as accurately as an LLM reranker at a third of the latency, and more accurately than a multi-call graph traversal.

论文介绍了 MiniCorp，一个用于研究 Agent 如何协作运营公司并规模化生成企业数据的办公模拟器，并对其端到端保真度相对于真实市场实证研究所报告的模式进行了评估。MiniCorp, an office simulator for studying how agents can collectively run a company while generating enterprise data at scale, is introduced and end-to-end fidelity against patterns reported in empirical studies of real markets is evaluated.

论文提出 Self-compensating VLA，一种部署阶段的自适应方法，使 VLA 策略在生成指令时能够预补偿机器人的执行误差，并在平均任务成功率上高于基线策略以及在训练阶段增强鲁棒性的方法。Self-compensating VLA is proposed, a deployment-time adaptation method that enables a VLA policy to pre-compensate for the robot's execution errors when generating commands, and achieves higher average task success than both the base policies and methods that build in robustness during training.

提出一种新颖的进化系统，专注于利用 RAG 增强的 LLM 自动生成空域隐写的代码级代价函数；该方法在隐写安全性上一致优于现有自动设计方法，同时提高了平均代码执行率并降低了搜索成本。A novel evolutionary system focused on exploiting Retrieval-Augmented Generation enhanced LLMs for the automatic code-level generation of spatial steganography cost functions, which consistently achieves higher steganographic security than existing automatically designed methods and increases the average code execution rate while reducing the search cost.

本文提出 DiffGate，一种将 GRPO 与选择性、有界教师指导相结合的结果门控目标，在四种模型–领域设置下均提升了 pass@8，表明在该评估协议下解的覆盖度得到改善。DiffGate is introduced, an outcome-gated objective that combines GRPO with selective, bounded teacher guidance, and improves pass@8 across all four model--domain settings, demonstrating improved solution coverage under the evaluation protocol.

场地特异性施肥推荐系统会根据地点、土壤属性、作物类型和生产目标调整养分建议，但当推荐功能主要通过交互式界面访问、输出未做版本管理、训练后的近似模型无法独立加载或基准测试时，其科学复用性受到限制。本技术报告介绍 Turba 施肥机器学习技术栈——面向摩洛哥场地特异性施肥推荐的可复现三层开源实现，其中 turba-client 提供对公开……Site-specific fertilizer recommendation systems adapt nutrient advice to location, soil properties, crop type, and production targets, but scientific reuse is constrained when recommendation functions remain accessible mainly through interactive interfaces, outputs are not versioned, and trained approximations cannot be independently loaded or benchmarked. This technical report presents the Turba fertilizer machine learning stack, a three-layer open-source implementation for reproducible site-specific fertilizer recommendation in Morocco. turba-client provides programmatic access to publicly a

自回归视频扩散支持流式生成与交互控制，但其 KV cache 随生成历史持续增长。现有压缩策略要么采用固定窗口丢弃历史，要么依据局部注意力与相似度信号选择 token，均无法直接衡量当前块是否贡献了超出已保留上下文的信息。本文提出 DeCoPrune，一种将 cache 压缩视为去噪一致性问题的免训练方法。经验上发现，去噪难度可作为 token 价值的有用代理……Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows continuously with the generated history. Existing compression strategies either discard history using fixed windows or select tokens through local attention and similarity signals, which do not directly measure whether the current chunk contributes information beyond the retained context. We introduce DeCoPrune, a training-free method that treats cache compression as a denoising-consistency problem. We find empirically that denoising difficulty provides a useful proxy for a token's valu

分布匹配蒸馏（DMD）通过分别估计的目标分数与学生分数之差训练少步学生模型，因而必须维护一个拟合学生动态分布的辅助扩散模型，带来额外的显存与计算开销。本文提出 DMAD——分布匹配对抗蒸馏，将分布匹配重构为分类任务，直接学习所需的 log-density 比。共享主干上的两个判别头分别区分真实数据与教师样本是否来自学生模型，并对其 logits 施加线性损失以训练……Distribution Matching Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it must keep an auxiliary diffusion model fitted to the student's evolving distribution at extra memory and computation cost. We introduce DMAD, Distribution Matching as Adversarial Distillation, which recasts distribution matching as classification and learns the required log-density ratios directly. Two discriminator heads on a shared backbone distinguish real data and teacher samples from the student's, and linear losses on their logits train the

提出 RoboDojo，一个面向通用机器人操作策略综合评估的仿真-真机统一基准，将 30 种策略集成到 XPolicyLab 并在 RoboDojo 上进行评测，建立了公开的排行榜与系统性的策略性能分析。RoboDojo is introduced, a unified sim-and-real benchmark for comprehensive evaluation of generalist robot manipulation policies that integrates 30 policies into XPolicyLab and evaluates them on RoboDojo, establishing a public leaderboard and systematic analysis of current policy performance.

将音乐转录为人类可读的乐谱需要对节奏、和声、旋律与曲式的整体理解。两大障碍限制了这一目标的实现：标注录音稀缺，以及精确的局部预测仍可能产生不一致的音乐序列。本文提出 SheetSage2，一个统一的音乐转录框架，结合合成数据、任务特定的结构化解码与自回归蒸馏。自动标注的 MIDI 渲染为音频后，可为各类音乐理解任务提供可扩展的监督。任务特定的结构化解码器整合互补的音乐……Transcribing music into a human-readable score requires a coherent understanding of rhythm, harmony, melody, and form. Two obstacles limit this goal: annotated recordings are scarce, and accurate local predictions can still produce inconsistent musical sequences. We present SheetSage2, a unified music transcription framework that combines synthetic data, task-specific structured decoding, and autoregressive distillation. Automatically annotated MIDI, rendered into audio, provides scalable supervision across music understanding tasks. Task-specific structured decoders integrate complementary mu

稀疏视图新视角合成是三维内容创作中的核心问题，但基于扩散的方法受迭代去噪限制，多视图生成在推理时开销高昂。本文提出 NAMVIS，一种无扩散框架，将多视图图像合成重新表述为几何条件下的下一尺度自回归过程。NAMVIS 不通过反复去噪生成目标视图，而是通过少量由粗到细的尺度步预测离散视觉 token，并在同一尺度内以及跨目标视图间并行采样所有 token。为锚定此过程……Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, making multi-view generation expensive at inference time. We introduce NAMVIS, a diffusion-free framework that reformulates multi-view image synthesis as geometry-conditioned next-scale autoregression. Instead of generating target views through repeated denoising, NAMVIS predicts discrete visual tokens through a small number of coarse-to-fine scale steps, while sampling all tokens within each scale and across target views in parallel. To anchor thi

视觉-语言-动作（VLA）基础模型规模迅速扩大以提升操作性能与泛化能力，但这种规模化带来了高昂的计算成本，使真实世界部署日益困难。现有方法通常通过设计更小的架构或减少基于流（flow-based）策略中的迭代去噪步数来缓解该问题。本文提出 FastOPD，一个从基础到轻量的 VLA 框架，通过高效的在线蒸馏实现大规模 VLA 的实际部署。具体而言，FastOPD 适配流映射（flow map）……Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs high computational costs that render real-world deployment increasingly challenging. Existing approaches typically mitigate this issue by designing smaller architectures or reducing the iterative denoising steps in flow-based policies. In this work, we propose FastOPD, a foundation-to-lightweight VLA framework that enables the practical deployment of large-scale VLAs through efficient on-policy distillation. Specifically, FastOPD adapts a flow map

WildCity 是一个由自动驾驶车队在复杂城市环境中采集的真实多模态数据集，旨在推动城市级渲染的进展，并更广泛地推动 AI 在空间感知、记忆与推理方面达到与人类认知相当规模的能力。WildCity, a real-world multimodal dataset collected by autonomous fleets traversing complex urban environments, aims to catalyze progress not only in city-scale rendering, but more broadly in the pursuit of AI that can perceive, remember, and reason across space at a scale comparable to human cognition.