论文

本文证明空间覆盖与三维推理性能相关，并提出 CoVeR，一种仅使用 token 坐标、不依赖学习信号的确定性、无训练选择器，在三项三维推理基准上均超越既有 SOTA，并可作为即插即用模块泛化到四种 VLM。It is shown that spatial coverage is associated with 3D reasoning performance and CoVeR, a deterministic, training-free selector that uses only token coordinates, with no learned signals is introduced, which outperforms prior SOTAs on all three 3D reasoning benchmarks and generalizes as a plug-and-play module tested across four VLMs.

本文提出 TANGO，首个面向语言条件人形机器人在杂乱环境中通行的全身视觉语言导航框架，在视觉语言导航任务中达到 SOTA，并在需要避障的困难场景中超越强模块化基线。This work introduces TANGO, the first whole-body vision-language navigation framework for language-conditioned humanoid traversal in cluttered environments, and demonstrates state-of-the-art performance in vision-language navigation, while outperforming strong modular baselines in navigating challenging scenes requiring obstacle negotiation.

TransNormal-2 是基于 FLUX.2 的整流流框架，采用单步确定性推理，针对 VAE 解码器两侧的重建退化问题，施加 RGB 引导的残差修正以降低局部于边界的解码误差，且不自由改写粗预测。TransNormal-2, a FLUX.2-based rectified-flow framework with single-step deterministic inference that addresses VAE reconstruction degradation on both sides of the VAE decoder, and applies an RGB-guided residual correction to reduce boundary-localized decoding errors without freely rewriting the coarse prediction.

本文提出 BeaconKV，一种免训练的 KV cache 压缩方法，通过为每个全局查询簇维护紧凑代表性 beacon query 来预测哪些 KV 对将被重访，无需存储完整查询历史。BeaconKV is proposed, a training-free KV cache compression method that maintains beacon queries, compact representatives for each global query cluster, to anticipate which KV pairs will be revisited without storing the entire query history.

提出 A*-Thought-V2，一个由 LLM 引导的几何动力学框架，将 CoT 建模为隐状态轨迹，并以显式-隐式交错潜在架构替代硬删除，引入更广义的软目标以促进更丰富的步骤级特征学习。A*-Thought-V2 is presented, a geometric dynamics of LLM guided framework that models CoT as a hidden-state trajectory and replaces hard deletion with an explicit-implicit interleaved latent architecture that reflects broader soft targets that encourage richer step-level feature learning.

发现空间步态参数对视觉域偏移更敏感，且更好的 HMR 重建并不一定带来下游步态估计的改进；提出 GaitXFormer，作为直接基于 RGB 的参考模型用于步态参数估计。It is found that spatial gait parameters are more sensitive to visual domain shift and that improved HMR reconstruction alone does not necessarily translate to improved downstream gait estimation, and GaitXFormer is introduced as a direct RGB reference model for estimating gait parameters.

提出 OpenWAM，一个开源研究栈，将世界-动作预训练转化为可控的实验项目，并发布完整栈，包括基础设施、评估协议、预训练模型和数据配方，以促进未来研究。This work introduces OpenWAM, an open research stack that turns world-action pretraining into a controlled experimental program, and releases the full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, to facilitate future research.

发现合作者专业能力在模型早期层最易被解码，到网络中点前降至接近随机水平，并基于合成语料以单个模型作为初步验证。It is found that partner expertise is most decodable in the early layers and falls to near chance before the midpoint of the network, and one model on a synthetic corpus is used as an initial demonstration.

提出 UCF-Net，一个不确定性感知级联融合网络，利用 CLIP 语言对齐的语义先验与 DINO 自监督视觉结构先验，在域内与跨域评估中均取得最优平均 AUC。UCF-Net is proposed, an uncertainty-aware cascaded fusion network that harnesses CLIP's language-aligned semantic priors and DINO's self-supervised visual-structure priors and achieves the best mean AUC among the evaluated methods in both in-domain and cross-domain evaluations.

提出一个前馈式生成 Transformer，用于直接的单视图与多视图图像重光照，完全绕过显式本征属性估计，并采用置换不变的位置编码对称处理无序多视图输入，避免序列偏差。This work introduces a feed-forward generative Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation and employs permutation-invariant positional encodings to symmetrically process unordered multi-view inputs without sequential bias.

我们提出 Cadence，一种面向数值时序的误差有界有损压缩器，将 3.3 亿参数的时序基础模型（Google TimesFM-3）与自适应算术编码器相结合，保证每个样本满足 |x_t - x̂_t| ≤ τ。一项负面结论限定了设计空间：在无损编码场景下，基础模型毫无价值，因为节省的比特数仅与预测器精度呈对数关系 Δb = log_2(MAE_old / MAE_new)。因此 TimesFM-3 相对 32 阶线性预测器 1.51 倍的精度优势，在 20.28 比特中仅换取 0.60 比特，中位数增益仅 +0.03%。误差有界编码仅在一点上突破了这一限制。We present Cadence, an error-bounded lossy compressor for numeric time series pairing a 330M-parameter time-series foundation model (Google TimesFM-3) with an adaptive arithmetic coder, guaranteeing |x_t-x_t|leτ on every sample. One negative result constrains the design space: for lossless coding a foundation model is worth nothing, because bits saved are logarithmic in predictor accuracy, Δb=log_2(MAE_{old}/MAE_{new}). So the 1.51times advantage TimesFM-3 holds over a 32-tap linear predictor buys 0.60 bits of 20.28, a median gain of +0.03%. Error-bounded coding escapes this at one point: once

Noah 是一个时间感知、任务无关的生成式 Transformer 模型，对完整多模态患者旅程进行表征与预测，是该领域首个真正整体化的生成模型，支持自回归预测，并具备可选的时间控制、零样本分类与反事实干预模拟能力。Noah is a time-aware, task-agnostic, generative transformer model representing and forecasting the full multimodal patient journey, and is the first truly holistic generative model in its field, enabling autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation.

本文为 LLM serving 中的 SD（投机解码）提出了一种简单且可解释的 latency 模型，能够准确刻画实际观测到的 latency，解释为何加速比常随服务器负载上升而下降，并系统刻画了 draft length、acceptance rate 以及 verifier 与 drafter 规模在不同 serving 条件下对 latency 的影响。A simple and interpretable latency model for SD in LLM serving is developed that accurately describes observed latency, explains why speedups often diminish as server load increases, and characterizes how draft length, acceptance rate, and verifier-drafter size shape latency across serving conditions are characterized.

主张防御的运行单元应是可修订的协同 episodes，将观察到的迁移、任务权限与响应历史关联起来，并提出跨执行监控建议具有可测试性，但并不声称提出新的检测器或测得具体的遏制收益。It is argued that the operational unit of defence should be a revisable coordination episode linking observed transfers, task authority, and response history, and it makes the recommendation to monitor across executions testable without claiming a new detector or a measured containment benefit.

我们提出 RenderFormer-V2，一个统一的基于 transformer 的学习型神经渲染模型，可与现代基于物理的渲染系统互补，无需逐场景训练或专用代码，即可处理焦散、体积散射、环境光照、带纹理与置换的表面以及分布外材质等多种光传输效果。RenderFormer-V2 将全局光传输建模为序列到序列变换。沿袭前作，它仍采用两阶段流程：先是与视图无关的阶段，解析场景内基元到……We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to modern physics-based rendering systems, that can handle diverse light-transport effects such as caustics, volumetric scattering, environment lighting, textured and displaced surfaces and out-of-distribution materials without per-scene training or specialized code. RenderFormer-V2 models global light transport as a sequence-to-sequence transformation. Following its predecessor, RenderFormer-V2 also employs a two stage process: a view-independent stage that resolves intra-scene primitive to

提出 Graph Machine，一种保持 O(n) 规模状态并通过稀疏动态路由访问的架构，使用边——由类似指针追逐的引用机制以可微分方式更新的指针类对象。The Graph Machine is introduced, an architecture that maintains an O(n)-sized state and accesses it through sparse, dynamic routing and uses edges - pointer-like objects updated differentiably by a referral mechanism resembling pointer chasing.

我们研究一种受治理的企业分析方法：语言模型负责解读问题，确定性 policy 负责选取并运行预先批准的分析程序，返回结果与证据。我们证明，在限定的分析类内（包含关系运算，以及聚合、比较、窗口、排序和相似度），这种限制仍可保持表达力。固定的语义、policy、数据和执行规则也使结果可复现。在 440 次运行中，三个 8B 模型生成 SQL 并在运行时选取工具，而 Qwen3-8B 仅解读意图，policyWe study a governed approach to enterprise analytics: a language model interprets the question, while deterministic policy selects and runs a pre-approved analytical program that returns both results and evidence. We show that this restriction can remain expressive within a defined analytical class, using relational operations plus aggregation, comparison, windows, ranking, and similarity. Fixed meaning, policy, data, and execution rules also make results replayable. Across 440 runs, three 8B models generated SQL and selected tools at runtime, while Qwen3-8B interpreted intent only and policy

Glyph 是一个生产系统，将列描述生成与列类型标注这两个耦合问题建模为协同工作的 LLM Agent，并以有状态图形式编排，使多 Agent LLM 目录编制可审计且可作为生产服务运行。Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs, makes multi-agent LLM cataloging auditable and operable as a production service.

评估 AI Agent 能否作为科学家利用 SAE 工具开展自主机理发现，旨在将实验性模型理解确立为可测量的能力，推动闭环自主 AI R&D。Whether AI agents can act as scientists utilizing SAE tools for autonomous mechanistic discovery is evaluated to establish experimental model understanding as a measurable capability for closed-loop autonomous AI R&D.

开发一条 on-policy 专家修正流水线，由元层级 MLE Agent 自动化，在弱模型自身的 rollout 中定位失败回合，并请专家仅重写该回合，从而保留模型的规划风格，融合 Harness 进化与模型适配带来的收益。An on-policy expert-correction pipeline is developed, automated by a meta-level MLE agent, that localizes the failing turn in the weaker model's own rollout and asks the expert to rewrite only that turn, which preserves the model's planning style and combines the gains of harness evolution and model adaptation.

Tutti 是一种高效的 SSD-backed KV caching 方案，将 CPU 从 HBM 与 SSD 之间的关键数据与 I/O 控制路径中彻底移除，在提供近乎无限容量的同时，实现了与 DRAM-backed LMCache 几乎相当的 inference 性能。Tutti is an efficient SSD-backed KV caching solution that eliminates CPU intervention from the critical data and I/O control paths between HBM and SSDs, and achieves nearly the same inference performance as DRAM-backed LMCache, while providing almost infinite capacity.

Discovery Certification Protocol（DCP）将结果声明转化为在已注册模型、信息边界与预算下的可执行审计，并由确定性验证器基于冻结记录复现本地决策The Discovery Certification Protocol (DCP) turns an outcome claim into an executable audit under a registered model, information boundary, and budget, and a deterministic verifier reproduces these local decisions from frozen records.

提出 DianShi-RxnDB，一个通过全自动抽取与归一化流水线（整合专利文本、图像和反应路线图）构建的大规模细粒度有机反应数据平台。D DianShi-RxnDB is presented, a large-scale, fine-grained organic reaction data platform built via a fully automated extraction and normalization pipeline integrating patent text, images, and reaction schemes integrating patent text, images, and reaction schemes.

本文介绍 Brain2Semantics2Text，一种通过中间语义嵌入空间重建文本的方法，并阐述了该方法的核心原理、实现方式以及缓解学习可靠神经-语义映射挑战的策略。This work introduces Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space and describes the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping.

提出 AgentGrad，一种基于序贯干预与语义文本梯度抽象的多智能体系统 prompt 优化框架，在 5 个 MAS benchmark 上取得 SOTA 性能，同时降低优化耗时与成本AgentGrad is proposed, a prompt optimization framework for multi-agent systems based on sequential intervention and semantic textual gradient abstraction that achieves state-of-the-art performance across five MAS benchmarks while reducing wall-clock optimization time and optimization cost.

这些结果表明，Kueue、DAS 与 GAIE 等互补组件构成了一个高性能的协同平台，证明了 Kubernetes 能够作为承载高要求 GenAI 工作负载的统一底座。These findings illustrate that these complementary components (Kueue, DAS, and GAIE) form a cohesive, high-performance platform, proving Kubernetes' capability to serve as a unified foundation for demanding GenAI workloads.

本文介绍 PARSER，将阅读与推理解耦，对证据位置、顺序与距离的扰动具有鲁棒性——这些条件会导致序列方法产生大幅精度波动——同时将推理延迟降低多达 11 倍。PARSER, which decouples reading from reasoning, is introduced, which is robust to perturbations in evidence position, order, and distance, conditions that cause large accuracy swings in sequential methods, while reducing inference latency by up to 11x.

本文引入影响引导的响应改写方法，利用 IF 识别干预目标，在保持指令不变的情况下将其响应替换为行为对齐或行为对立的监督信号，以此推动对 TDA 方法的干预感知评估。Influence-guided response rewriting is introduced, which uses IF to identify intervention targets and replaces their responses with behavior-aligned or behavior-opposed supervision while keeping instructions fixed, motivating intervention-aware evaluation of TDA methods.

本文介绍 AgentZip，第一个专为 AI Agent 沙箱设计的内存压缩系统，将压缩范围扩展到任何具有收益表示的页面，并将开销控制从压缩时页面选择转移到恢复时预取。AgentZip is presented, the first memory compression system designed specifically for AI-agent sandboxes, which broadens the compression scope to any page with a profitable representation and shifts overhead control from compression-time page selection to restore-time prefetching.

在真实的博卡拉湖畔地理环境中，由 100 个配备记忆机制的大语言模型 Agent 管理一个封闭且守恒的空间经济，并运行该多 Agent 模拟长达 26 个模拟周，远超典型 Agent 社会研究 1–2 周的时长。100 memory-equipped large language model agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1-2 weeks typical of agent-society studies.

提出 X-AuT，一个渐进式框架，通过简短的行为探针选择层组合，并通过表征对齐、跨尺度蒸馏、调度式学生策略监督以及 LoRA 微调来恢复被剪枝的模型。X-AuT, a progressive framework that selects layer combinations through short behavioral probes and restores the pruned model through representation alignment, cross-scale distillation, scheduled student-policy supervision, and LoRA finetuning, is introduced.

Generative Late-Interaction Embeddings（GLIE）：从归一化质心中学习每个页面 k<<N 个向量，既作为轻量级索引，也作为重建页面完整嵌入集的基础，解码器是其主要设计面。Generative Late-Interaction Embeddings (GLIE): k<<N vectors per page learned from the normalized centroids to serve as both a lightweight index and a basis for regenerating the page's full embedding set, with the decoder as its main design surface.

提出一个用于困难奥林匹克数学自然语言证明生成的开放模型测试时计算流水线，完全在自然语言中运行，无需形式化证明器、外部工具或互联网访问。An open-model test-time-compute pipeline for natural-language proof generation for hard olympiad mathematics that operates entirely in natural language, with no formal prover, external tools, or internet access is presented.

本文提出了一种简单的 flow-control 框架，通过控制 prompt 加入 LLM 活跃集合的速率，实现更高的 token 与 request 吞吐量、更低的平均与尾部 latency，以及更稳定的 KV cache 利用率。A simple flow-control framework is proposed that controls the rate at which prompts join the active set in large language models and achieves higher token and request throughput, lower average and tail latency, and more stable KV cache utilization.

引入 SpatialBlock-15k，一个包含 15,000 个堆块问题的合成数据集，涵盖 3D 到 2D 投影、视角变换和结构组合，并提出受人类认知发展启发的新范式：通过结构化堆块操作任务学习基础空间技能。This work introduces SpatialBlock-15k, a synthetic dataset of 15,000 block-stacking problems covering 3D-to-2D projection, viewpoint transformation, and structural combination and proposes a novel paradigm inspired by human cognitive development: learning foundational spatial skills through structured block-manipulation tasks.

提出 MaP-WAM，一个将记忆作为规划的 Memory-as-Plans 框架，将依赖记忆的世界动作建模分解为基于记忆的规划和以规划为条件的执行，并以长期多模态情景上下文作为规划时证据，而非反复对执行器输入完整历史。MaP-WAM is introduced, a Memory-as-Plans framework that decomposes memory-dependent world-action modeling into memory-grounded planning and plan-conditioned execution, and uses long-term multimodal episodic context as planning-time evidence rather than repeatedly conditioning the executor on the full history.