论文

提出 DiSCO，一种零样本、严格黑盒的防御方法，完全在提示层面以即插即用模块的形式运行，无需模型重训练、微调或访问模型内部，可直接应用于任何文本到图像系统，无需对模型本身进行任何修改。DiSCO is proposed, a zero-shot, strictly black-box defense that operates entirely at the prompt level as a plug-and-play module, requiring no model retraining, fine-tuning, or access to model internals, and can be readily applied to any text-to-image system without necessitating any changes to the model itself.

结果表明，Engram 可充当可复用的外部知识工件，前提是目标侧具备兼容的 Reader 接口；当直接复用 Reader 效果不足时，目标侧适配可进一步改善对齐效果。The results suggest that Engram can serve as a reusable external knowledge artifact, provided that the target has access to a compatible reader interface and target-side adaptation can further improve alignment when direct reader reuse is insufficient.

本文系统性地研究了视觉编码器扩展中的 MoE 设计，发现细粒度 MoE 拓扑相较于稠密与标准 MoE 基线均带来显著提升；提出了一种无辅助损失的均衡变体以改善专家利用率，并设计了专用 MoE kernel 以缓解推理时延开销。This work systematically study MoE designs for vision encoder scaling and finds that fine-grained MoE topologies yield substantial gains over both dense and standard MoE counterparts, and proposes an auxiliary-loss-free balancing variant for better expert utilization, and designs a specialized MoE kernel to mitigate inference latency overhead.

本文提出了 LIFE-RL 框架，在不修改内部控制流的前提下，将原生编码 Agent harness 与可扩展的策略梯度优化相连接，并通过 GSPO 在三个原生编码 Agent harness 上训练稀疏 MoE 模型 Qwen3.5-35B-A3B 对其进行了评估。LIFE-RL, a framework that bridges native coding-agent harnesses with scalable policy-gradient optimization without modifying their internal control flow, is presented and evaluated by training the sparse MoE model Qwen3.5-35B-A3B with GSPO across three native coding-agent harnesses.

受搜索与推荐系统启发，本文构建了 Find、Attempt 与 Recommend（FAR），即一个从文献到综述的级联流程，可自动搜索合适的问题，并将人类注意力聚焦于经过多阶段筛选的成果上。Inspired by search and recommender systems, this work builds Find, Attempt, and Recommend (FAR), a literature-to-review cascade that automates the search for suitable problems and focuses human attention on artifacts that have passed several stages of filtering.

本文提出 Co-RL，一种由多个解耦模型组成的框架，这些模型不共享参数，通过基于彼此输出奖励的强化学习同时进行优化，并表明无监督推理可以通过协作式多智能体训练涌现This work introduces Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers, and shows that unsupervised reasoning can emerge through cooperative multi-agent training.

动作条件目标改善了基于欧几里得代价与 CEM 的潜在 MPC 所使用的几何结构，DA-LeWM 在 LeWM 基础上增加了逆动力学和演示条件的目标-动作头，加速了收敛并取得比 LeWM 更高的在线成功率Action-conditioned objectives improve the geometry used by Euclidean-cost, CEM-based latent MPC, and DA-LeWM augments LeWM with inverse-dynamics and demonstration-conditioned goal-action heads, and accelerates convergence and achieves higher online success than LeWM.

本文提出 Zetta，一种闭环具身 harness，在保持基础策略冻结的同时在线演化基于代码的运行时评判器与恢复技能，表明闭环 harness 的自进化为可靠的物理智能开辟了一条可扩展的路径Zetta is presented, a closed-loop embodied harness that evolves code-based runtime critics and recovery skills online while keeping the base policy frozen, and shows that closed-loop harness self-evolution opens a scaling path for reliable physical intelligence.

实验表明，在该数据上微调的模型不仅持续优于基座模型和面向写作的专门基线，还优于基于现有写作语料训练的模型，表明受控的题材扩展是稳健创意写作能力的关键驱动力Experiments demonstrate that models fine-tuned on the data consistently surpass not only base models and writing-specialized baselines, but also models trained on existing writing corpora, indicating that controlled genre expansion is a key driver of robust creative writing capability.

本文提出Larch，一个用于优化AI SQL查询中语义过滤器执行的框架，并给出其两种变体：Larch-A2C与Larch-Sel，二者在token使用量上均始终优于现有语义过滤器优化技术。This paper introduces Larch, a framework for optimizing the execution of semantic filters in AI SQL queries and presents two Larch variants: Larch-A2C and Larch-Sel, which always outperform existing semantic filter optimization techniques in terms of token usage.

本文提出 OmniScientist，一种端到端、全模态 AI 科学家，可直接基于异构原始证据开展跨学科研究，表明全生命周期感知对于基于证据的科学发现至关重要，并为构建广泛适用的 AI 科学家提供了一条切实可行的路径OmniScientist is introduced, an end-to-end, omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence and demonstrates that lifecycle-wide perception is essential for evidence-grounded scientific discovery and provides a practical path toward broadly capable AI scientists.

论文认为，agentic CTI 调查的瓶颈在于该 substrate 而非模型能力，并提出了面向 Agent 的语料库脚手架 CTIFoundry。It is argued that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and CTIFoundry, an agent-native corpus scaffold, is presented.

SkillGate 将 9B 策略的成功率从 40.8% 提升至 53.2%，显著优于将相同预算仅用于 outcome reward 的方案，同时将误导性候选的暴露减少三分之二，并读取更少的 skill。SkillGate lifts a 9B policy from 40.8% to 53.2% trial success, well ahead of the identical budget spent on outcome reward alone, while cutting exposure to misleading candidates by two thirds and reading fewer skills.

提出 VA-Judger，一种链式思考的通用奖励模型：从质量差距明显的样本对中学习以建立结构化输出和粗粒度偏好判别，再通过拒绝采样（以人类标注验证）蒸馏出可靠的偏好解释用于更难的质量相近样本比较，最后执行维度级强化学习，将人类反馈分解到各独立质量维度以获得更稠密的奖励信号。VA-Judger is proposed, a chain-of-thought omni-reward model that learns from pairs with clear quality gaps to establish structured output and coarse preference discrimination, then distills reliable preference explanations for harder near-quality comparisons via rejection sampling verified against human annotations, and finally performs dimension-wise reinforcement learning that decomposes human feedback into individual quality dimensions for denser reward signals.

提出 RapidLiDAR，一种将初始化本身视为可学习、数据驱动组件的 LiDAR 场景补全方法，在与 SOTA 相当的补全性能下，0.1 秒完成整个场景，比此前最快方法快 2.3 倍。RapidLiDAR is presented, a LiDAR scene completion method that treats the initialization itself as a learned, data-driven component and achieves completion performance on par with the state of the art while completing a full scene in 0.1 seconds, which is 2.3 times faster than the fastest prior method.

受损模型评估测试在第一个合法 tool call 之后插入 ground-truth 攻击调用，从而独立于模型行为测试 APC，证明了 APC 实现的 Blast Radius 单调性与组合可靠性。The compromised-model evaluation tests APC independently of model behavior by inserting the ground-truth attack call after the first legitimate tool call, which proves Blast Radius Monotonicity and Composition Soundness for APC implementations and proves Blast Radius Monotonicity and Composition Soundness for APC implementations.

实验表明，DataEvolver 显著提升了数据质量，相比在原始数据上训练，下游 LLM 性能平均提升 10%，凸显了 LLM 与数据迭代协同演化的新机遇。Experiments show that DataEvolver substantially improves data quality and achieves an average 10\% gain in downstream LLM performance compared with training on original data, highlighting new opportunities for the iterative co-evolution of LLMs and data.

4DAnyone 在新视角视频质量和下游 4DGS 重建上均优于先前方法，并具有稳健的野外泛化能力。4DAnyone outperforms prior methods in both novel-view video quality and downstream 4DGS reconstruction, with robust in-the-wild generalization.

提出 Repo0，一个面向零到全代码生成的持续结构演化框架，维护显式的架构状态，实例化为双有向无环图（Dual-DAG），由需求级 DAG、组件级 DAG 及其对齐关系组成。Repo0 is presented, a continuous structural evolution framework for zero-to-all code generation that maintains an explicit architectural state instantiated as a Dual-Directed-Acyclic-Graph (Dual-DAG), consisting of a requirement-level DAG, a component-level DAG, and their alignment relation.

本文提出 IAR（Inject, Align, and Recover），一个三阶段后训练框架，将结构化文档知识注入、问答行为对齐与通用能力恢复解耦，提升面向无检索文档内化的"领域主—领域通"前沿。This work proposes IAR (Inject, Align, and Recover), a three-stage post-training framework that separates structured document knowledge injection, QA behavior alignment, and general ability recovery and improves the domain-primary domain-general frontier for retrieval-free document internalization.

本文研究 LLM 在测试时从迭代经验中学习的机制，称之为 Chain-of-Experience (CoE)：模型通过与自身或环境反馈的迭代交互积累经验痕迹，形成超越零样本推理的持续改进循环。This study studies how LLMs learn from iterative experience at test time, a setting the authors refer to as Chain-of-Experience (CoE), where models accumulate experiential traces through iterative interactions with self or environmental feedback to form a continual improvement loop beyond zero-shot inference.

本文指出，持续技能演化的关键瓶颈既非编辑能力亦非迭代轮数，而在于评估反馈能否持续提供可信的演化梯度；据此提出 SkillEvo，由可信反馈生成梯度，由可控治理约束方向。This work argues that the binding constraint on sustained skill evolution is neither editing capability nor the number of iterations, but whether the evaluation feedback keeps supplying trustworthy evolution gradients, and introduces SkillEvo, in which trustworthy feedback generates the gradient and controllable governance constrains its direction.

泄露带来两类现实攻击：一个训练好的分类器可从常规自然语言输出中推断用户记忆的语义谓词；一个由 RL 训练的对抗者可从生产级风格的 Agent 中完整提取社会安全号码。Leakage enables two practical attacks: a trained classifier that infers semantic predicates about user memories from routine natural-language outputs, and an RL-trained adversary that extracts full Social Security Numbers from a production-style agent.

本文提出 NAPE（Next-Audio-Patch-Embedding prediction），一个自监督框架：因果 Transformer 仅依据因果掩码与 stop-gradient，从先前 patch 嵌入预测对数梅尔频谱图的下一 patch 嵌入。NAPE (Next-Audio-Patch-Embedding prediction), a self-supervised framework in which a causal Transformer predicts each next patch embedding of a log-mel spectrogram from the previous ones, using causal masking and stop-gradient as its sole training signal is introduced.

Bespoke-Card 在传统通用估计器与学习型估计器架构之外开辟了一条新的基数估计路径，它是一个 Agent 驱动的系统，将面向特定工作负载的基数估计器合成为可执行代码。Bespoke-Card is opening a new avenue for cardinality estimation next to classical generic estimators and learned estimator architectures, an agent-driven system that synthesizes workload-specific cardinality estimators as executable code.

本文为阿拉伯法学（fiqh）构建了一个检索测试集，并基于此评估稠密、词法、混合、微调及教法学派感知（madhhab-aware）等检索策略；错误分析表明，主要挑战在于区分包含答案的段落与主题相似但不含目标教法的段落。This work builds a retrieval test collection for Arabic fiqh and uses it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies, and presents an error analysis showing that the main challenge is distinguishing answer-bearing passages from topically similar passages that do not contain the requested ruling.

FlowEvo 是一个免训练框架，在推理时让工作流与技能协同进化：它将成功的工作流编译为可调用技能，存入持久化技能库，并通过直接执行或作为上下文来检索使用这些技能，以构建新的工作流。FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time, compiles successful workflows into callable skills, stores them in a persistent bank, and uses retrieved skills either through direct execution or as context for constructing new workflows.

在领域内和分布偏移设置下，增加测试时计算可显著提升下一子任务预测准确率，这些增益进一步转化为长时序机器人操作任务中更高的闭环成功率。Across in-domain and distribution-shifted settings, allocating additional test-time computation substantially improves next-subtask prediction accuracy, and these gains translate into higher closed-loop success on long-horizon robot manipulation tasks.

我们提出 TinyCast，一个注意力无关的零样本预测器，仅用 146,505 个参数输出预测分布，其前提是在该规模下，上下文中的周期结构值得通过计算而非学习方式得到。一个零参数谱检测器给出主导周期，上下文按其相位进行折叠，再由一个膨胀卷积编码器和一个分块自回归分位数解码器建模其余部分。它在 GIFT-Eval 榜单上所有可确认参数量的零样本条目中体积最小；在概率准确性方面，它划定了 size-accuracy 前沿。We introduce TinyCast, an attention-free zero-shot forecaster that emits a predictive distribution from 146,505 parameters, on the premise that at this size the periodic structure of a context is worth computing rather than learning. A zero-parameter spectral detector supplies the dominant periods, the context is folded on their phase, and a dilated convolutional encoder and a block-autoregressive quantile decoder model the rest. It is smaller than every zero-shot entry on the GIFT-Eval board whose parameter count can be established. On probabilistic accuracy it defines the size-accuracy front

这些结果支持一种分工：使用 embedding 模型处理相似度、分类与聚类任务，将 LLM 留给推理密集型的检索任务。These results support a division of labour: use embedding models for similarity, classification, and clustering, and reserve LLMs for reasoning-intensive retrieval, and reserve LLMs for reasoning-intensive retrieval.

对指令下发 Agent 的评估应报告模型配置、生成契约、执行路径、工作点及终态校验器，而不应将匹配分数视为模型的内在属性。Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state validator rather than treat a matched score as an intrinsic model property.

本文提出 EnSI-RAG（Entity-Structure-Indexed Retrieval-Augmented Generation），通过构建查询无关、以实体为中心的索引，将证据定位与答案合成解耦，同时保留可追溯的源证据。This work proposes EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index that separates evidence localization from answer synthesis while preserving traceable source evidence.

本文提出 AID-Guard，一种有状态的授权到效果（authorization-to-effect）闭包协议：在提交（commit）时重新校验已批准的请求与提供方状态；在歧义下仅保留一个预订；仅在收到终态结果或经认证的“无效果”并设置投递栅栏（delivery fence）后，才允许释放或生成一个后继。This work presents AID-Guard, a stateful authorization-to-effect closure protocol that revalidates the approved request and provider state at commit, retains one reservation under ambiguity, and permits release or one successor only after a terminal result or certified no effect with a delivery fence.

结果显示，规约规模本身并不能预测实现质量，跨 Agent 迁移可能导致显著的、依赖具体 Agent 的性能下降；因此在异构 SDD 工作流中，不应将规约默认视为与 Agent 无关的工件（artifact）。The results show that specification size alone does not predict implementation quality and that cross-agent transfer can produce substantial agent-dependent degradation, and suggest that specifications in heterogeneous SDD workflows should not automatically be treated as agent-neutral artifacts.

PhysCaP 在 code-as-policy 框架上增加了物理信息驱动的探索层，使其能够通过交互进行显式的信息获取，并引入免训练的物理属性提取模块，仅凭机器人本体感知即可估计物体质量与刚度，无需额外传感器。PhysCaP augments code-as-policy frameworks with a physics-informed exploration layer that enables explicit information-seeking through interaction, and introduces training-free physical property extraction modules that estimate object mass and stiffness from robot proprioception without additional sensors.

本文提出 SparsePR，一种无需训练的方法，将响应耦合划分（Response-Coupled Partitioning）与探针拟合残差重建（Probe-Fitted Residual Reconstruction）相结合，并发现划分方式既影响同一分组内查询偏好支持集之间的重叠程度，也影响稀疏输出的仿射函数对稠密与稀疏输出差异的拟合能力。This work introduces SparsePR, a training-free method combining Response-Coupled Partitioning with Probe-Fitted Residual Reconstruction, and finds that partition choice affects both the overlap among grouped queries' preferred supports and how well an affine function of the sparse output can represent the difference between dense and sparse outputs.