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.

VA-Judger：用于联合视频-音频生成的思维链全模态奖励模型。它首先从具有明显质量差距的数据对中学习，以建立结构化输出与粗粒度偏好判别；然后通过对照人工标注进行拒绝采样，蒸馏得到针对更难近质量对比的可靠偏好解释；最终执行按维度分解的强化学习，将人类反馈分解为各个质量维度以获得更密集的奖励信号。VA-Judger, a chain-of-thought omni-reward model for joint video-audio generation that first 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.

RAG 被用于伊斯兰问答，但多数系统采用端到端评估，难以区分检索失败与生成失败。本文研究阿拉伯 fiqh 的"承载答案的检索"——仅当段落陈述问题所需裁决时才视为相关。我们构建了阿拉伯 fiqh 检索测试集，并评估 dense、lexical、hybrid、fine-tuned 及 madhhab-aware 检索策略。最佳检索器 MRR@5 达 0.524，fine-tuning 进一步提升至 0.553；hybrid retrieval 增益有限（原文截断）。Retrieval-Augmented Generation is used for Islamic question answering, but most systems are evaluated end-to-end, making retrieval failures difficult to isolate from generation failures. We study answer-bearing retrieval for Arabic fiqh, where a passage is relevant only if it states the ruling required by the question. We build a retrieval test collection for Arabic fiqh and use it to evaluate dense, lexical, hybrid, fine-tuned, and madhhab-aware retrieval strategies. The best retriever achieves 0.524 MRR@5, while fine-tuning improves performance to 0.553. Hybrid retrieval provides limited gai

大语言模型 Agent 可在推理时构建工作流以适配复杂任务，但单个回合中发现的过程通常在执行后即被丢弃。现有 Skill 库提供可复用的可执行例程，但通常离线组装，且无法从 Agent 自身的工作流中不断生长。我们提出 FlowEvo，一个无需训练、推理时工作流与 Skill 协同演化的框架。FlowEvo 将成功的工作流编译为可调用 Skill，存入持久化库，并通过直接执行或作为上下文的方式使用检索到的 Skill。Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time. FlowEvo compiles successful workflows into callable skills, stores them in a persistent bank, and uses retrieved skills either through direct execution or as context

长周期机器人操控要求机器人既能可靠执行各项技能，又能在一系列长时间任务中将其连贯编排。多数分层视觉-语言-动作（VLA）模型仅通过单次前向过程做出每个决策，缺乏将额外算力分配给困难或关键抉择的机制。我们提出 τ_0-VLA，一种分层机器人基础模型，将高层子任务生成建模为可通过世界模型引导的测试时计算来扩展算力的推理问题。在每次推理时，高层策略借助执行……Long-horizon robot manipulation requires a robot to both execute individual skills reliably and sequence them coherently over extended tasks. Most hierarchical vision-language-action (VLA) models make each such decision with a single forward pass, leaving no mechanism to allocate additional computation to difficult or consequential choices. We introduce τ_0-VLA, a hierarchical robot foundation model that formulates high-level subtask generation as a compute-scalable inference problem through world-model-guided test-time computation. At each inference step, the high-level policy uses execution

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

是否应将文本嵌入流水线替换为 LLM？我们对此在 37 个任务（涵盖分类、语义文本相似度 STS、聚类、配对分类与检索）上，对覆盖 6 个家族、参数规模 118M 至 14B 的 10 个 LLM 与 26 个嵌入模型进行了受控且考虑成本的对比。总体上两种范式基本持平：最佳 LLM（Gemini 3.1 Pro，77.6）与最佳嵌入模型（77.2）仅相差 0.4 分。两者在不同任务上各有所长：LLM 在推理密集型检索上领先，嵌入模型则在分类任务上领先，且两者……Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval. In aggregate the two paradigms are effectively tied: the best LLM (Gemini 3.1 Pro, 77.6) and the best embedding model (77.2) differ by 0.4 points. Their strengths differ by task: LLMs lead on reasoning-heavy retrieval, embedding models lead on classification, and th

LLM 编程 Agent 通过接口下发 Bash 命令，这些接口可能对模型输出进行序列化、包装与再解析。仅凭匹配的执行得分无法区分命令生成阶段的错误与生成后引入的失败。QuoteBench 围绕一道刻意未转义的额外解析器，将生成合约与执行传输交叉，在 14 个源自真实事件的家族、共 56 个一次性任务上以精确最终状态校验来度量该边界。在插值点进行转义可复现每条回放回复的原始路径结果，因此任何在已声明边界下取得的恢复成效都必须……LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply's raw-path outcome, so any recovery under a disclosed boundary must com

长篇连通文档上的 QA 仍具挑战，因为相关证据可能跨越多个实体及其关系。现有 RAG 方法通常将文档以原始 chunk 索引并通过 embedding 相似度检索，当 chunk 边界切断实体与支持证据的联系，或问题需在语料库中多跳推理时性能下降。我们提出 EnSI-RAG（Entity-Structure-Indexed RAG），一个构建 query-independent、以实体为中心的[索引]框架……Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationships. Existing retrieval-augmented generation (RAG) methods typically index documents as raw chunks and retrieve them through embedding similarity. Their performance degrades when chunk boundaries separate entities from supporting evidence or when a question requires multi-hop reasoning across the corpus. We propose EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered in

使用工具的 AI agents 将 delegated 任务转化为 provider effects，但 authorization 常止于准入，而 provider 状态、delivery、retry 与 recovery 在持续演化。请求可能在 commit 前变化，或响应丢失使替换从同一 approval 产生二次 effect。我们提出 AID-Guard，一种有状态的 authorization-to-effect 闭合协议：commit 时重新验证已批准请求与 provider 状态，模糊情形下仅保留一个 reservation，并在出现终结结果或经 delivery fence 认证的无 effect 后才允许 release 或一次后继动作。对于支持的 pro[vider]……Tool-using AI agents turn delegated tasks into provider effects, yet authorization often ends at admission while provider state, delivery, retry, and recovery evolve. A request may change before commit, or response loss may cause a replacement to create a second effect from one approval. We present AID-Guard, a stateful authorization-to-effect closure protocol. It 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. For supported pro

本文以 Oracle-to-PostgreSQL 迁移为受控软件转换任务，研究跨 agent 的 specification portability。研究包含两个阶段：首先在 1,006 个 PL/SQL 文件上评估 specification-first 迁移 pipeline，其中 623 个成功重新生成，380 个生成脚本在 PostgreSQL 16 中成功执行；其次使用 Amazon Kiro、Google Gemini 与 GitHub Copilot（含 Claude Code 与 Cursor）在 1,802 个 Oracle 脚本及其对应 PostgreSQL 实现的[数据集]上进行跨 agent 实验。This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The study combines two experimental stages. First, a specification-first migration pipeline was evaluated on 1,006 PL/SQL files, of which 623 were successfully regenerated and 380 generated scripts executed successfully in PostgreSQL 16. Second, cross-agent experiments were conducted on a dataset of 1,802 Oracle scripts with corresponding PostgreSQL implementations using Amazon Kiro, Google Gemini, and GitHub Copilot, with Claude Code and Cursor inclu

我们提出 PhysCaP，一种面向机器人操作主动感知的物理信息驱动 code-as-policy agent。尽管 vision-language-action 策略擅长模仿示教，但它们依赖被动观察，无法推断对操作至关重要的潜在物理属性。PhysCaP 在 code-as-policy 框架基础上引入物理信息驱动的探索层，通过交互实现显式信息获取。它提出了无需训练的物理属性提取模块，仅依靠机器人本体感知即可估算物体质量和刚度，无需额外传感器。为了在 balanWe present PhysCaP, a Physics-Informed Code-as-Policy agent for active perception in robotic manipulation. While vision-language-action policies excel at imitating demonstrations, they rely on passive observation and fail to infer latent physical properties critical for manipulation. PhysCaP augments code-as-policy frameworks with a physics-informed exploration layer that enables explicit information-seeking through interaction. It introduces training-free physical property extraction modules that estimate object mass and stiffness from robot proprioception without additional sensors. To balan

免训练的块稀疏注意力可加速视频 Transformer，但仅凭逐行注意力集中度本身无法确定一个可执行的稀疏算子。共享同一块路由路径的查询其支撑集可能重叠很差，而仅保留的注意力质量并不足以决定由跳过的交互所产生的 softmax 后误差。我们证明划分几何同时影响池化支撑集与从稀疏输出预测剩余残差的能力。我们提出 SparsePR，将响应耦合划分与探测拟合残差重构相结合。采样查询键Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries sharing a block route may have poorly overlapping supports, while retained attention mass alone does not determine the post-softmax error from skipped interactions. We show that partition geometry affects both pooled support and the predictability of the remaining residual from the sparse output. We introduce SparsePR, which combines Response-Coupled Partitioning with Probe-Fitted Residual Reconstruction. Sampled-query ke