论文

该工作提出 AutoTraceGT（Automated Trace analysis through Grounded Theory），首个在 Agent 轨迹上自动化 grounded theory 的多 Agent pipeline，并指出 Grounded Theory 为研究 Agent 实际行为的 ML 研究者和 Agent 开发者提供了可扩展的分析工具。This work proposes AutoTraceGT (Automated Trace analysis through Grounded Theory), the first multi-agent pipeline that automates grounded theory on agent trajectories and suggests Grounded Theory offers a scalable analytic tool for ML researchers and agent developers studying what agents actually do.

提出 QCell，一种新颖的基于查询的模型，用于在显微镜场景中去重叠细胞实例，在多个 benchmark 上优于 SOTA 方法，在 ISBI2014 上取得 +2.2 AP 和 +2.7 AJI。QCell is presented, a novel query-based model that de-overlaps cell instances in microscopy scenes and outperforms state-of-the-art methods across multiple benchmarks, achieving +2.2 AP and +2.7 AJI on ISBI2014.

该工作提出 DRACO：Distributing Rubric-based Advantage for Credit Optimization，在训练期间动态生成 rubric 以追踪 policy 的演化能力，对已完成的轨迹一次性评分，并将该判断重新分配到负责标注 rubric 的步骤上，以在 GRPO 中产生差异化的 per-step advantage。This work proposes DRACO: Distributing Rubric-based Advantage for Credit Optimization, which generates rubrics dynamically during training to track the policy's evolving capability, scores those rubrics once per completed trajectory, and redistributes that judgment over the steps responsible for annotated rubrics to produce differentiated per-step advantages in GRPO.

提出 VeriPhy，一种可审计的物理验证系统，其中纯文本 planner 在观察任何帧之前将 prompt 编译为类型化的物理义务与静态验证的执行计划。VeriPhy, an auditable physical-verification system in which a text-only planner compiles the prompt into typed physical obligations and a statically validated execution plan before any frame is observed, is presented.

表面 prompting 未能恢复多样性，而针对入口的干预则成功：使用早期 checkpoint 的 late-layer parameter interpolation 在不损失 pass@1 的情况下将解的覆盖度提高了 37%。While surface prompting fails to recover diversity, entrance-targeted interventions succeed: late-layer parameter interpolation with early checkpoints increases solution coverage by 37% at no loss in pass@1 and late-layer parameter interpolation with early checkpoints increases solution coverage by 37% at no loss in pass@1.

本工作提出 RoboTok，一个可扩展的数据引擎：利用人类操作视频作为查询，从互联网检索与操作相关的演示以训练灵巧机器人策略，并从以演员为中心的参考系下估计的 3D 手部轨迹中学习一个潜在运动空间This work introduces RoboTok, a scalable data engine that uses a query human manipulation video to retrieve manipulation-relevant internet demonstrations for training dexterous robot policies and learns a latent motion space from 3D hand trajectories expressed in estimated actor-centered reference frames.

Tangram 是一种 serving 框架，将先前系统动态处理的内容静态解析，可作为现有 non-uniform 压缩方法的即插即用底座，在匹配其精度的同时，端到端吞吐量较 full-KV 基线最高提升 2.6×。Tangram is a serving framework that statically resolves what prior systems handle dynamically, and serves as a drop-in substrate for existing non-uniform compression methods, matching their accuracy while improving end-to-end throughput by up to $2.6\times over the full-KV baseline.

涵盖数学推理、多领域 STEM、代码生成以及多轮 Agent 任务的实验表明，RISE 在所有设置下均优于仅使用 RLVR 的训练以及 on-policy self-distillation。Experiments spanning mathematical reasoning, multi-domain STEM, code generation, and multi-turn agentic tasks show that RISE outperforms RLVR-only training and on-policy self-distillation across all settings.

该工作提出了 MaxKernel，一个多 Agent 系统，实现了 TPU kernel 开发的 three distinct paradigms：Human-in-the-Loop (HITL) Agent，用于协作式分步设计；Autonomous (Auto) Agent，执行全自动、由指标和 trace 驱动的优化循环；以及 Graph-Based Autonomous Search，将 Auto Agent 扩展以对设计空间进行全局探索。This work presents MaxKernel, a multi-agent system that implements three distinct paradigms for TPU kernel development: a Human-in-the-Loop (HITL) agent for collaborative, step-by-step design; an Autonomous (Auto) agent that executes a fully automated, metric/trace-driven optimization loop; and a Graph-Based Autonomous Search that scales the Auto agent for global exploration of the design space.

该工作通过向参考解中注入受控的 AST 级 corruption，从 400 个 BigCodeBench 问题构建了一个评估框架，为每个修复任务赋予已知的最小 patch，并将 edit fidelity 定位为 code-repair 质量的一个独立维度，表明其可被度量与学习。This work constructs an evaluation framework from 400 BigCodeBench problems by injecting controlled AST-level corruptions into reference solutions, giving each repair task a known minimal patch, and positions edit fidelity as a distinct axis of code-repair quality and shows that it can be measured and learned.

循环状态写回被确立为低精度循环动态的关键决定因素，state-storage interface 被识别为量化循环推理的核心设计考量。R recurrent-state write-back is established as a key determinant of low-precision recurrent dynamics and the state-storage interface is identified as a central design consideration for quantized recurrent inference.

提出了 Motion-Omni，一个端到端框架，其中的 spoken dialogue model 原生输出显式的 facial expression 以及手部、上半身和下半身运动，这些输出直接由生成语音的 hidden states 生成。Motion-Omni is presented, an end-to-end framework in which a spoken dialogue model natively outputs explicit facial expression together with hand, upper-body and lower-body motion, generated directly from the hidden states that produce the speech.

该工作发现 reasoning operations 在 held-out 表征中是可分的，且 separability 在中间层达到峰值，并验证该结构无法由词汇或位置混淆因素解释。This work finds that reasoning operations are separable in held-out representations, with separability peaking in middle layers, and verify that this structure is not explained by lexical or positional confounds.

研究揭示沿音视频边的路由是双向的：音频可影响视频生成，视频也可影响音频生成；模型参数中编码的偏差是泄漏的主要来源之一。It is revealed that routing along the audio-video edge is bidirectional: audio can influence video generation, while video can influence audio generation, and biases encoded in the model's parameters and emerges as a major contributor to leakage.

本文认为，AI agent——即以大语言模型作为主要推理引擎、动态生成与丢弃代码作为工具性资源的系统——的出现构成了对"软件"本身的根本性重构，而非渐进式的工具改进。This paper argues that the emergence of AI agents -- systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource -- constitutes a fundamental restructuring of what software is, not an incremental tool improvement.

HoloWorld 是首个在统一连贯的 3D 城市世界中同时支持室内与室外生成的框架，构建于持续更新的跨尺度世界上下文之上。HoloWorld is the first framework to unify indoor and outdoor generation within a coherent 3D urban world, built on a continuously updated cross-scale world context.

提出 UniMate，一个统一的 foundation model，可从绑定骨骼的 3D 资产与文本提示合成任意骨骼的关节运动，无需测试时优化或针对每个骨骼的重新训练，在质量、泛化性与效率上均超越 SOTA 基线。UniMate is presented, a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton retraining, and outperforms state-of-the-art baselines in quality, generalization, and efficiency.

本文将安全微调数据集中的回复拆分为两个独立部分：模板化的拒答声明与解释拒答的理由，并表明拒答声明会诱导模型依赖表层线索，从而妨碍对有害与良性查询的准确区分。This paper decomposes a response in the safety-tuning dataset into two distinct components: a boilerplate refusal statement and a rationale explaining the refusal, and shows that refusal statements impede accurate discrimination between harmful and benign queries by inducing reliance on superficial cues.

视频编辑涵盖多种编辑范式，但在单一统一框架中同时实现高质量的指令引导与主体引导编辑仍具挑战性。我们提出 EditVid，一个免训练框架，结合用于局部一致性的稀疏因果记忆、用于长程身份保持的基于对应关系的后注意力 token 注入，以及用于编辑局部性的软潜在融合。同一框架支持指令引导和参考引导的编辑，包括风格迁移、属性修改、对象插入、部分级编辑和主体替换。在Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On

为推进任务课程聚焦于大规模词分类，本竞赛设置两条互补赛道：Deep 赛道面向同一被试内部的大规模词分类，目标是追求最佳性能；Broad 赛道面向跨被试泛化。Advancing the curriculum of tasks to focus on word classification to focus on word classification at scale, two complementary tracks are presented in this competition: the Deep track targets within-subject word classification at scale, aiming at the best possible performance; the Broad track targets cross-subject generalisation.

提出 FactoSR，一个因子化强化学习框架，显式解读视觉投影所塌缩的维度，并指出强化显式的、因子化的 4D 一致性是将 VLM 演化为稳健、具有世界感知能力的推理器的关键一步。FactoSR is presented, a factorized reinforcement learning framework that explicitly interpret the dimensions collapsed by visual projection, and suggests that reinforcing explicit, factorized 4D consistency is a critical step toward evolving VLMs into robust, world-aware reasoners.

本文提出 Dr. Claw，一个开源工作空间，将现有编码 Agent 可执行文件封装在可控且可审计的人机协同工作流中，而非引入另一个自主 Agent。Dr. Claw is presented, an open-source workspace that wraps existing coding-agent executors in a controllable and auditable human-in-the-loop workflow rather than introducing another autonomous agent.

本文提出 Memanto，一种面向 agentic 人工智能的通用记忆层，挑战了"必须依赖知识图谱复杂度才能实现高保真 agent 记忆"的普遍假设，并取得 SOTA 准确率。Memanto is introduced, a universal memory layer for agentic artificial intelligence that challenges the prevailing assumption that knowledge graph complexity is necessary to achieve high fidelity agent memory and achieves state of the art accuracy scores.

FlowBalance 是一种以验证器为锚定的自改进方法，学习完整响应上的归一化分布，在 Qwen3-4B 和 Qwen3-8B 上相对 FlowRL 提升了平均性能，同时改善了训练速度与稳定性，避免了直接 OPSD 响应长度坍缩，并在受控的 AIME24 诊断中表现出更高的正确策略多样性。FlowBalance, a verifier-grounded self-improvement method that learns a normalized distribution over complete responses, improves average performance over FlowRL on both Qwen3-4B and Qwen3-8B, while also improving training speed and stability, avoiding direct OPSD's response-length collapse, and exhibiting higher correct-strategy diversity in a controlled AIME24 diagnostic.

本文为该设定引入新基准，并基于该基准评估了九种修订方法，包括序贯反思与并行采样变体，使用 gpt-oss-20b/120b、gpt-5.4-mini 以及 qwen3.5-9b/27b/122b 进行测试。A new benchmark for this setting is introduced, and nine revision methods are evaluated, including sequential reflection and parallel sampling variants, using gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b on the benchmark.

本文提出 Teacher-Gated On-Policy Distillation（教师门控的在线策略蒸馏），其核心原则是在引入密集监督前以 prompt 级别验证教师可靠性；在全部六个单领域设定下优于 Vanilla OPD，并在多领域训练下于两种规模上取得更高的七项基准平均成绩。Teacher-Gated On-Policy Distillation is introduced, built on the principle that teacher reliability should be verified at the prompt level before dense supervision is admitted, and outperforms Vanilla OPD in all six single-domain settings and achieves higher seven-benchmark averages at both scales under multi-domain training.

本文提出 KnowChange，一个知识引导的变化数据合成框架，利用预训练视觉语言模型作为知识源，从变化前场景与期望变化类型推理合理的变化位置与类别转移，在统一框架下灵活合成多样变化类型。KnowChange is introduced, a knowledge-guided change data synthesis framework that leverages pretrained vision-language models as knowledge sources to reason about plausible change locations and class transitions from pre-change scenes and desired change types and enables flexible synthesis of diverse change types within a unified framework.

大语言模型（LLM）的成功很大程度上归功于 next-token prediction（NTP），但其自回归（AR）结构需要缓慢的串行 token 生成。为克服这一瓶颈，我们提出扩散增强 LLM，一类新模型，在使用扩散从该分布中并行采样多个 token 的同时定义 AR 模型分布。我们将这些模型的参数解耦为两组：AR 权重，使用标准 NTP 目标训练；轻量扩散权重，训练用于同时生成多个 token。扩散权重Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diffusion-augmented LLMs, a new class of models that defines an AR model distribution while using diffusion to draw multiple tokens in parallel from that distribution. We decouple the parameters of these models into two sets: AR weights, trained using the standard NTP objective, and lightweight diffusion weights, trained to generate multiple tokens simultaneously. The diffusion weight

我们提出 ENEAS，一种用于实例追踪与语义发现的统一且文本可提示的方法。包括 SAM 3 在内的文本可提示分割模型仍存在时间幻觉、空间碎片化与语义误分类问题：目标离开视野时无法报告目标缺失；极端特写下只分割局部纹理而非完整目标；将视觉特征置于本体事实之上，从而把雕像、绘画或反射等视觉相似的物体误分割为目标。We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the latest foundation models such as SAM 3, still suffer from temporal hallucinations, spatial fragmentation, and semantic misclassification: they fail to report target absence when an object leaves the field of view, segment local textures instead of the complete object during extreme close-ups, and prioritize visual features over ontological reality, so that visually similar artifacts such as statues, paintings, or reflections are segmented as targe

本文对一个双节点 split-LLM 训练系统进行系统安全案例研究：其隐私评估通过，却遗留一条未被测试的可观测信道；系统因此并不安全——包括跨训练步骤累积观测在内的五类攻击从未被测量。A systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel untested, but the system is not thereby safe: five classes of attack, including those accumulating observations across training steps, were never measured.

本文提出 MatryoshkaLoRA，一种受 Matryoshka 启发、面向 LoRA 的通用训练框架，通过在已有 LoRA adapter 之间插入一个固定的、经精心设计的对角矩阵来按比例缩放其子秩，从而学习到准确的层次化低秩表示。MatryoshkaLoRA is proposed, a general, Matryoshka-inspired training framework for LoRA that learns accurate hierarchical low-rank representations by inserting a fixed, carefully crafted diagonal matrix between the existing LoRA adapters to scale their sub-ranks accordingly.

因果基础模型是经过预训练的神经网络，可在全新的数据集上通过上下文学习估计因果量（如平均处理效应），无需模型更新。Causal foundation models are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates.

本文提出 Conformal Relevance 框架，利用上下文学习的示例筛选与集成构造打分函数，在保持覆盖的同时以极低人工成本提升简洁性。The Conformal Relevance framework is introduced which uses in-context learning example curation and ensembling to create a score function which maintains coverage while improving conciseness with minimal manual input.

提出了 Gander，一个原生多模态双工交互模型，基于 MiniCPM-o 4.5 构建，并通过异步 Agent 循环进一步适配实时交互，同时开源其模型、代码和数据，以推动社区进一步研究与开发。Gander is presented, a native multimodal duplex interaction model that builds on MiniCPM-o 4.5 and is further adapted for realtime interaction with an asynchronous agent loop and is released together with its models, code, and data to facilitate further research and development in the community.

Mask Forcing 是一种双噪声掩码展开策略，通过扰动自回归学生的自展开来缓解由 reverse-KL 模式寻求引发的模式坍缩，能以更高视觉质量高效改进多种自回归视频扩散蒸馏方法，且无需引入真实视频数据或额外后训练阶段。Mask Forcing, a Dual-Noise Masking Rollout strategy that perturbs the AR student self-rollout to mitigate mode collapse induced by reverse-KL mode seeking, improves multiple AR video diffusion distillation methods with higher visual quality efficiently, without incorporating real video data or additional post-training stages.

本文提出 AuK，一个开源基础模型，通过自然语言指令与音频上下文的统一接口整合语音生成与编辑，在零样本与指令控制的语音生成以及通用指令引导编辑上取得领先性能。AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context, is introduced and leading performance on zero-shot and instruction-controlled speech generation and general instruction-guided editing is demonstrated.