论文

Lean Pool 是一个形式化数学仓库，由 AI agent 进行生长、维护和优化。Lean Pool is a repository of formalized mathematics. It is grown, maintained and optimized by AI agents.

本文提出了 SEGMENT-SNAP，通过 part-handle coupling 融合几何与语义证据，在 Articulate3D Challenge 中获得第一名。This work presents SEGMENT-SNAP, which combines geometric and semantic evidence through part-handle coupling and achieved first place in the Articulate3D Challenge.

本文提出一种面向 few-shot 图像分类的超轻量级空间-关系架构，结合固定的 Gabor 边缘能量引导与窗口化的、内容自适应的 patch locator。实验表明，该架构中虽然存在 pairwise relational computation，但它并非性能的主要驱动因素；真正起决定作用的是 content-adaptive patch locator。An ultra-lightweight spatial-relational architecture for few-shot image classification that combines fixed Gabor edge-energy guidance with a windowed, content-adaptive patch locator is presented, showing that the architecture's pairwise relational computation, while present, is not the primary driver of its performance; the content-adaptive patch locator is.

本文揭示 Agent 数量是多 Agent 组织扩展通用智能边界的新 scaling 维度，为硬延迟约束或时间预算下的复杂任务提供了实用方案。The number of agents is revealed as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets.

LLM-as-a-judge 支持跨任务评估，但在大规模场景下推理成本与置信度可靠性成为关键问题。本文研究仅做判断的 judge 能否提供经济高效的首轮判别，并识别何时需要更强的评估。在与十六种生成式与奖励模型 judge 的对比中（采用盲法人类裁定作为参照），我们发现 jev-as-a-judge 在普通偏好与有证据支撑的事实性任务上，与作为最强对照的 SOTA LLM judge 仅相差 3 个百分点，成本仅为后者的 0.36%。在需要核查LLM-as-a-judge enables evaluation across diverse tasks, but inference cost and confidence reliability become critical at scale. We study whether a decision-only judge can provide an economical first pass and identify when stronger evaluation is needed. Comparing jev-as-a-judge with sixteen generative and reward-model judges, with blinded human adjudication, we find it within three percentage points of a state-of-the-art LLM judge, our strongest comparator, on ordinary preference and evidence-grounded factuality at 0.36% of the comparator's fee. Larger gaps arise when judgments require checking

一种近期且有效的方法：通过一个小型 low-rank adapter，将大型预训练图像编辑模型适配到图像恢复任务，并以源自退化图像本身的 instruction 替代 text prompt；在同等条件下该方法优于文本条件。A recent and effective recipe adapts a large pretrained image-editing model to restoration using a small low-rank adapter with a text prompt to an instruction derived from the degraded image itself that outperforms text conditioning under a matched comparison.

本文展示了在一对架构匹配的 Qwen3.5 4B-to-9B 兄弟模型之间实现有用的持久 hybrid-state transfer，是首次在大小不同的混合语言模型之间完成持久循环推理状态的跨模型交接，且无需 target prefix replay。This work demonstrates useful persistent hybrid-state transfer across one architecture-matched Qwen3.5 4B-to-9B sibling pair, the first demonstrated cross-model handoff of persistent recurrent inference state between differently sized hybrid language models without target prefix replay.

TritonForge 是一个面向自动化 Triton kernel 优化的 profiling 引导框架，融合 kernel 分析、运行时 profiling 与迭代式代码转换以简化优化流程，并为自动化 GPU 性能优化领域的未来研究奠定基础。TritonForge, a profiling-guided framework for automated Triton kernel optimization that integrates kernel analysis, runtime profiling, and iterative code transformation to streamline the optimization process and provides a foundation for future research in automated GPU performance optimization.

Just-in-Time Memory (JitMem) 一致优于无 memory 的 Agent 以及启发式与学习式写入 memory 方法，相较最强基线在成功率上分别提升了 16.2、16.3 和 3.9 个绝对百分点。Just-in-Time Memory (JitMem) consistently outperforms no-memory agents as well as heuristic and learned write-time memory methods, improving over the strongest baseline by 16.2, 16.3, and 3.9 absolute success-rate points, respectively.

论文证实，AI 辅导在 GRE 学习收益上与专家人类辅导具有统计等效性（p = .015），且在 GRE 的七个领域中，有五个领域的最佳 AI 导师平均超越了人类导师。It is established that AI tutoring is statistically equivalent to expert human tutoring for GRE learning gains (p = .015), and in five of the seven GRE domains, the best performing AI tutor surpassed the human tutor, on average.

PackLab 是一个用于开发、训练与评估闭环机器人装箱 MLLM 的综合框架，在不同物体集合与容器配置下均优于传统装箱启发式方法、经典强化学习方法以及通用 MLLM，展现了 MLLM 在长时任务机器人装箱中的潜力。PackLab is a comprehensive framework for developing, training, and evaluating MLLMs for closed-loop robotic bin packing that outperforms conventional packing heuristics, traditional reinforcement learning methods, and general-purpose MLLMs across object sets and container configurations, highlighting the potential of MLLMs for long-horizon robotic packing.

介绍 Spatial-Interactor，一个通过交互训练 VLM 建模物理世界状态转换的框架，并将学习过程组织为三级课程：L1 被动世界状态转换、L2 主动自我状态转换、L3 长时交互轨迹。Spatial-Interactor is introduced, a framework that trains VLMs to model physical-world state transitions through interaction, organizing this learning process into a three-level curriculum covering L1 passive world-state transitions, L2 active self-state transitions, and L3 long-horizon interaction trajectories.

本文探讨容器化、微服务、动态调度等 Cloud Native 技术如何从根本上提升 LLM 推理服务，并展示 Cloud Native 系统在高需求场景下实现更高效资源分配、降低延迟与提升吞吐的能力。This article explores how Cloud Native technologies, such as containerization, microservices, and dynamic scheduling, can fundamentally improve LLM inference serving and demonstrates how a Cloud Native system enables more efficient resource allocation, reduces latency, and enhances throughput in high-demand scenarios.

FLEET 将每次生成表示为穿越状态的稀疏轨迹（状态的熵超过预设阈值），并基于这些轨迹推断逐 token 的效用分数以调整 logits，是一种将 memory 机制融入生成过程的新方法。FLEET represents each generation as a sparse trajectory through states whose entropy exceeds a predefined threshold and uses these trajectories to infer per-token utility scores that adjust the logits, a novel method that integrates a memory mechanism into the generation process.

本文提出一种方法，通过逐层剔除后 WER（Word Error Rate）的变化对编码器层进行排序，并给出剪枝后的模型——即一个层数更少的更浅编码器。This work presents an approach that ranks encoder layers by the leave-one-layer-out change in Word Error Rate (WER), and presents the pruned model, which is simply a more shallow encoder with fewer layers.

本文提出一个基于原理的、无需训练的框架，依次优化跨层权重配对与共享字典分解，并识别结构兼容的投影、学习一种能更好保留各层独立校准几何的共享表征。This work introduces a principled, training-free framework that sequentially optimizes cross-layer weight pairings and shared-dictionary factorizations, and identifies structurally compatible projections and learns a shared representation that better preserves each layer's distinct calibration geometry.

结果支持将生成式记忆视为对直接检索的选择性修正，并强调何时、以何种方式、以何种强度进行路由干预是核心挑战。The results support generated memory as a selective correction to direct retrieval and highlight routing when, which, and how strongly to intervene as the central challenge.

本文提出 X-Planner，一个面向具身推理的规划前端，同时解决监督与表征问题，并描述了规划文本质量与下游执行情况。This work presents X-Planner, a planning front-end that addresses both the supervision and representation of embodied reasoning, and describes planning-text quality and downstream execution.

结果发现 Astra 表现出 token 高效的有向推理，能更早选择正确轨迹，在内部解决基础步骤，仅外化关键推理，为前沿模型推理提供了超越基准分数的行为视角。It is found that Astra exhibits token-efficient directed reasoning, selecting a correct trajectory earlier, while resolving elementary steps internally and externalizing only crucial reasoning, which provides a behavioral lens on frontier-model reasoning beyond benchmark scores.

Knowledge Pull Requests 相比从来源重写或从零重新生成，能整合更多信息并更好地保留已有内容，同时每生成一个 token 增加的信息量最多。Knowledge Pull Requests integrate more information and better preserve existing content than rewriting from sources or regenerating from scratch, while adding the most information per token generated.

本文首次对 vLLM 启动延迟进行了详细的性能表征，并构建了一个轻量级分析模型，能够针对给定硬件配置准确预测 vLLM 的启动延迟，为大规模推理环境中的资源规划提供了可操作的指导。This paper presents the first detailed performance characterization of vLLM startup latency and develops a lightweight analytical model that accurately predicts vLLM's startup latency for a given hardware configuration, providing actionable guidance for resource planning in large-scale inference environments.

本文提出 PUBG Ally，一个面向 PUBG: BATTLEGROUNDS 的具身代理，能推理、自主行动并作为语音队友与玩家并肩作战，将代理式工具使用与实时游戏控制相结合。PUBG Ally is introduced, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate that combines agentic tool use with real-time game control.

本文提出 World Action Agent，一个多代理框架，通过它 VLM 可借助基础工具操控机器人，所有决策均在可视化动作工作空间内完成，在相同骨干下优于端到端 VLA、code-as-policy 代理以及一个可视化框架基线。World Action Agent is presented, a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace, outperforming end-to-end VLAs, code-as-policy agents, and a visual-harness baseline with the same backbone.

主要发现是：多样且高质量的 SFT 奠定坚实的能力下限，难度过滤将 RL 提示维持在有效的学习区间内，而奖励可靠性为阶段排序提供了实用原则。The main findings are that diverse, high-quality SFT establishes a strong capability floor and difficulty filtering keeps RL prompts within a productive learning range, and reward reliability provides a practical principle for ordering stages.

本文提出 ViRDM，一种无需教师与评论家网络的视频后训练方案，将三网络蒸馏转化为仅生成器的后训练，在降低 GPU 显存与训练时间的同时提升视频质量。ViRDM, a teacher- and critic-free video post-training recipe that turns three-network distillation into generator-only post-training, reducing GPU memory use and training time while improving video quality, is introduced.

本文提出 Neural Spectral Capacity (NSC)，一种以每个权重矩阵的奇异值谱为依据的闭式标量，在七类 Transformer 与 CNN 系列上的排序效果优于 #Params、#FLOPs 及代表性免训练代理指标。This work proposes Neural Spectral Capacity (NSC), a closed-form scalar grounded in the singular-value spectrum of each weight matrix, which outperforms #Params, #FLOPs, and representative training-free proxies in ranking across seven Transformer and CNN families.

结果显示 SAE 以分布式、类别依赖的形式定位形态句法信息，而非通过原子化的语法特征。The results show that SAEs localise morpho-syntactic information in a distributed and category-dependent form rather than through atomic grammatical features.

本文将数据安全形式化为 provenance monomials 上的聚合谓词，并提出 Passant——一个无需物化 provenance 即可强制执行 DFC 策略的可移植查询重写层。This paper formalizes data safety as aggregate predicates over provenance monomials and presents Passant, a portable query rewriting layer that enforces DFC policies without materializing provenance.

本文发现编码代理在通用 TAMP 上表现出惊人的有效性：在平均成功率上，三种代理配置均优于人工设计的规划器、一次性生成以及基于 LLM 的通用规划基线。This work finds that coding agents are surprisingly effective at generalized TAMP: all three agent configurations outperform hand-engineered planners, one-shot generation, and an LLM-based generalized planning baseline in mean success.

本文提出 DeltaWAM，通过 dense-anchor、sparse-delta 与 action 三条流联合预测视觉 delta 与动作，并设计了三种在表征与计算共享上有所不同的架构；同时开发了 Streaming Delta Memory (SDM)，使用紧凑的观测 delta 更新缓存的 anchor 上下文，从而减少繁重的 video-expert 处理。This work proposes DeltaWAM, which jointly predicts visual deltas and actions using dense-anchor, sparse-delta, and action streams, with three architectures that differ in representation and computation sharing, and develops Streaming Delta Memory (SDM), which updates cached anchor context with compact observed deltas, reducing heavy video-expert processing.

本文提出 AV-GRPO，一种以模态为锚点的在线 diffusion RL 框架，以及 5DAV，一种解耦的、难度可调节的训练数据集；在 LoRA 与全量 fine-tuning 条件下，其在生成质量、语义对齐和跨模态同步性上均优于 LTX-2.3。This work proposes AV-GRPO, a modality-anchored online diffusion RL framework, and 5DAV, a decoupled, difficulty-controllable training dataset that outperforms LTX-2.3 in generation quality, semantic alignment and cross-modal synchronization under LoRA and full fine-tuning.

近年来，大语言模型（LLMs）解决高级数学问题的能力日益增强，包括许多悬而未决数十年的难题。这为以空前规模扩展数学知识打开了大门。然而，尽管 LLM 能够猜想并证明越来越多的定理，这些新数学知识是否有趣或有用仍属未知。我们将定理的内在有趣度定义为其证明长度与陈述长度之比。证明该指标与下载量的外在度量高度相关。Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to conjecture and prove more and more theorems, it remains open whether this new mathematical knowledge is interesting or useful. We define intrinsic interestingness of a theorem as the ratio between the length of its proof and the length of its statement. We show that this correlates strongly with an extrinsic measure of the downs

本文提出一种可调深度网络架构，借助适配器残差模块可在线切换至不同视觉域；同时引入 Visual Decathlon Challenge 基准，用于评估表征同时捕获十个差异显著视觉域的能力，并衡量其跨域均匀识别的能力。This paper develops a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains and introduces the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very differentVisual domains and measures their ability to recognize well uniformly.

本文提出 PISA，一种采用金字塔式 Top-K 选择策略的 block-sparse attention 机制，并为训练与推理开发了硬件感知的 Triton kernel，将层级路由与 LogSumExp 评分融合，无需显式构造 query-key score 矩阵。PISA is proposed, a block-sparse attention mechanism that employs a pyramid Top-$K selection strategy, and develops hardware-aware Triton kernels for both training and inference, fusing hierarchical routing and LogSumExp scoring without materializing the query-key score matrix.

本文提出 ZooWork-ShopRanker，一族对齐到裁判标注购物偏好的电商 reranker，以及 ShopRank-Bench，一个包含约 10,000 条私有流量偏好对的低污染 benchmark，按承诺该标注的裁判家族数量分档呈现，覆盖多种文本格式。ZooWork-ShopRanker, a family of e-commerce rerankers aligned to judge-labeled shopping preference, and ShopRank-Bench, a contamination-limited benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label.

TrackEverything 是首个在 40 GB GPU 显存内即可在超过 1000 帧的视频中跟踪所有可见点的 3D tracker；本文还提出 3D WAFT，用场景点云内的高效特征采样取代了显存开销巨大的 4D correlation volumes。TrackEverything is the first 3D tracker capable of tracking all visible points across videos exceeding 1000 frames within 40 GB of GPU memory, and 3D WAFT is proposed, replacing memory-prohibitive 4D correlation volumes with efficient feature sampling in the scene cloud.