论文

SMRC-SD（State-Matched Routing and Contextualized Self-Distillation）显式地决定特权轨迹应在何时、以何种方式指导 on-policy student，其表现始终优于无条件的成功全路径蒸馏。State-Matched Routing and Contextualized Self-Distillation (SMRC-SD), which explicitly determines when and how a privileged trajectory should guide an on-policy student, consistently outperforms unconditional successful full-path distillation.

往返一致性将可逆性转化为生成式模型一种实用的可信信号；双向训练带来负成本，在两个方向上均优于单向专家模型；其中反向还可作为快速的逆问题求解器。Round-trip consistency turns reversibility into a practical trust signal for generative models, and Bidirectional training comes at negative cost, beating direction specialists in both directions, and the backward direction doubles as a fast inverse solver.

本文在低 prefill 延迟约束下优化 Pareto 前沿以最大化精度，提出 CoinRAG（Contextualized Information Nugget KV Cache Reuse for Long-Context RAG），通过 chunk 级上下文无缝拼接其切片化的 KV 表示。This paper optimizes the Pareto frontier under low prefill latency constraints while maximizing accuracy by proposing CoinRAG (Contextualized Information Nugget KV Cache Reuse for Long-Context RAG), which seamlessly assembles their sliced KV representations with a chunk-level context.

本文提出一种结构化协议，通过对开源 LLM 评估与安全工具的分类驱动分析来自动化 AI 风险缓解，并给出一个可同时适用于开源与商用方案的分类驱动框架。This paper proposes a structured protocol to automate AI risk mitigation through a taxonomy-driven analysis of open-source LLM evaluation and security tools, and presents a taxonomy-driven framework applicable to open-source and proprietary solutions.

通过分别评估论文检索、证据 grounding 与答案准确性，LitTraceQA 为生成可验证答案、而非无依据摘要的科学 QA 系统提供了测试基准。By evaluating paper retrieval, evidence grounding, and answer accuracy separately, LitTraceQA provides a testbed for scientific QA systems that produce verifiable answers rather than unsupported summaries.

本文提出 Exact Adaptive Hybrid Retrieval（EAHR），将完整列表加权 RRF 定义的有序 Top-K 固定为检索目标，并将通道深度视为请求特定的执行状态，在全部 150 组 query-snapshot 组合中复现了完整列表的有序 Top-20。This work proposes Exact Adaptive Hybrid Retrieval (EAHR), which fixes the ordered Top-$K$ defined by complete-list weighted RRF as the retrieval target and treats channel depth as request-specific execution state and reproduced the complete-list ordered Top-20 in all 150 query-snapshot combinations.

HiSparse 已合入上游 SGLang，并在 H200、B200 与 GH200 平台上针对 DSA、NSA、Quest 三类稀疏注意力家族进行评估：在长上下文负载下峰值生成吞吐提升最高达 4.7×，同时保持可比的 per-token 延迟，并降低高负载下的 time-to-first-token。HiSparse is merged into upstream SGLang and evaluated across three sparse-attention families (DSA, NSA, and Quest) on H200, B200, and GH200 platforms: it improves peak generation throughput by up to 4.7x on long-context workloads while preserving comparable per-token latency and reducing time-to-first-token at high load.

本文是一项关于如何提升蒸馏训练效率的实践研究，围绕两项系统贡献展开，并提出一种融合的 chunked KL loss，使峰值内存随序列长度线性增长。A practitioner's study of how to make distillation training efficient is presented, organised around two systems contributions, and a fused, chunked KL loss is introduced, making peak memory linear in the sequence length.

研究发现，微调后的 AO 可能变成概念特异的 anti-reader：它们会选择性地无法恢复自身训练过程中持续存在的概念，从而对习得的可解释性接口提出可靠性担忧。It is found that fine-tuned AOs can become concept-specific anti-readers: they selectively fail to recover the concept persistently present during their own training, raising a reliability concern for learned interpretability interfaces.

本文通过深度多模态嵌入视觉与自然语言数据，提出了一种用于图文双向检索的模型，并引入结构化的最大间隔目标，使该模型能够显式地跨模态关联片段。This work introduces a model for bidirectional retrieval of images and sentences through a deep, multi-modal embedding of visual and natural language data and introduces a structured max-margin objective that allows this model to explicitly associate fragments across modalities.

论文证明，使用标注数据进行微调，并允许模型查询外部知识源，能够在安全性和事实性这两个关键挑战上带来显著提升。It is demonstrated that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.

本文利用源自 CNN 的同一基础信息重新审视初始搜索与重排序两个检索阶段，显著改进了现有基于 CNN 的识别流水线。This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN, and significantly improves existing CNN-based recognition pipeline.

通过引入扩散卷积运算，本文展示了如何从图结构数据中学习基于扩散的表示，并将其作为节点分类的有效基础。Through the introduction of a diffusion-convolution operation, it is shown how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification.

Manifold Mixup 在监督学习、对单步对抗攻击的鲁棒性、半监督学习以及留出样本的负对数似然（NLL）上，相较强基线均取得了大幅提升。Manifold Mixup achieves large improvements over strong baselines in supervised learning, robustness to single-step adversarial attacks, semi-supervised learning, and Negative Log-Likelihood on held out samples.

本文介绍了 CodeXGLUE，一个基准数据集，旨在推动面向程序理解与生成的机器学习研究，涵盖 14 个数据集上的 10 项任务，并提供模型评估与比较的平台。This paper introduces CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation that includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison.

本文提出新的计算机视觉基础模型 Florence，通过融入来自 Web 规模图文数据的通用视觉-语言表示，将表征范围从粗粒度（场景）扩展到细粒度、从静态（图像）扩展到动态（视频）、从 RGB 扩展到多种模态（描述、深度等）。This work introduces a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine, from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth), by incorporating universal visual-language representations from Web-scale image-text data.

于 2018 年 8 月 28 日中午 12:15 在 Pettit 微电子研究中心 102 A/B 室进行报告。Presented on August 28, 2018 at 12:15 p.m. in the Pettit Microelectronics Research Center, Room 102 A/B.

本文提出了不变风险最小化（IRM），一种用于在多个训练分布上估计不变相关性的学习范式，并展示了 IRM 学到的不变性如何与数据的因果结构相关，从而实现分布外泛化。This work introduces Invariant Risk Minimization, a learning paradigm to estimate invariant correlations across multiple training distributions and shows how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.

本文在每个时间步以骨骼作为输入，引入一种新的正则化方案来学习骨骼关节的共现特征，并提出一种同时作用于 LSTM 神经元门、单元和输出响应的新型 dropout 算法。This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.

研究表明 BigBird 是序列函数的通用逼近器，且具备图灵完备性，从而保留了二次全注意力模型的这些性质。It is shown that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model.

提出 FaceVid-Forensics-100K，一个大规模深伪视频数据集，包含 100,000 个视频，涵盖 33 种合成方法，覆盖换脸、表情重演与全脸合成；同时提出一个多智能体取证推理框架，由四个领域专家 Agent 分别从四个角度独立分析伪造线索。FaceVid-Forensics-100K is introduced, a large-scale deepfake video dataset comprising 100,000 videos and spanning 33 synthesis methods across face swapping, face reenactment, and entire-face synthesis, and a multi-agent forensic reasoning framework that employs four specialized domain-expert agents to independently analyze forgery cues from four perspectives.

提出 Decoupling CLI Agent Scaffolding（DCAS），一种后端替换的拦截层，可在不修改 scaffold 的前提下，在任意 CLI scaffold 与任意后端模型之间路由 API 流量，从而支持跨 scaffold 评估与具备规划感知的轨迹采集。Decoupling CLI Agent Scaffolding (DCAS) is introduced, a backend-substitution interception layer that routes API traffic between any CLI scaffold and any backend model without modifying the scaffold, enabling cross-scaffold evaluation and planning-aware trajectory collection.

本文提出 Enfold，将构建未来的计算转移到由当前视觉上下文和语言指令预测出的表征中，并把世界生成器重塑为预测控制表征的来源，前提是其内部结构可被折叠（enfold）到当下。This work presents Enfold, which transfers this computation that constructs a future into a representation predicted from the current visual context and language instruction, and recast a world generator as a source of predictive control representations if its internal structure can be enfolded into the present.

训练一个紧凑分类器，对被省略的句子按其是否为理解保留文本所必需进行排序，并在推理时无需支撑标注地回插排名靠前的候选，同时优化相关性与指代完整性。A compact classifier is trained to rank omitted sentences by whether they are needed to interpret retained text and reinsert the top-ranked candidates without support annotations at inference, training a compact classifier to optimize both relevance and referential completeness.

CLIP-CC-Bench 为长视频描述提供了一个实用的评估框架，填补了现有短片段和仅 QA 基准的空白，并通过评分者间一致性（inter-judge agreement）与 bootstrap 排序稳定性量化该协议的内可靠性。CLIP-CC-Bench provides a practical evaluation framework for long-form video description, filling a gap left by existing short-clip and QA-only benchmarks and quantifying the protocol's internal reliability through inter-judge agreement and bootstrap ranking stability.

本文提出 MatrAIx，一个面向异构用户的群体规模模拟用户评估基础设施，为使用多样化模拟人类用户评估 AI 系统和数字产品提供端到端支撑。MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.

本文提出 Self-Modulating QKAN-based FWPs，用低秩生成的逐元素调制（作用于新提案分支、有界旧状态分支或两者）替代该广播门，并提出 Complementary Matrix Gating (CMG)，在保持标量门控的有界凸更新和仿射前缀扫描结构的同时提供逐坐标的记忆控制。This work introduces Self-Modulating QKAN-based FWPs, which replace this broadcast gate with low-rank-generated element-wise modulation of the new-proposal branch, a bounded old-state branch, or both, and proposes Complementary Matrix Gating (CMG), which provides coordinate-wise memory control while preserving the bounded convex update and affine prefix-scan structure of scalar gating.

本文提出 DuplexGen，一个通过少量 slot 级人类偏好标注对 LLM 预测进行校准从而生成具有场景自适应轮换特性的对话框架；结果表明，使轮换合成具备场景特异性的关键是人类校准，而非单纯的语料规模或提示设计。DuplexGen is introduced, a framework for generating dialogues with scenario-adaptive turn-taking by calibrating LLM predictions against a small set of slot-level human preference annotations, and results show that human calibration, not corpus scale or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.

Evo-Bench 是首个跨 Search、Office 和 General Agent 领域评估模型内在 harness 演化能力的基准，并暴露了早期饱和等关键时序异常，同时证明所合成的 harness 是高度可迁移的推理结构，能持续提升多样化策略模型。Evo-Bench is the first benchmark designed to evaluate models'intrinsic harness-evolving capabilities across Search, Office, and General agent domains, and exposes critical temporal anomalies like early saturation, while demonstrating that the synthesized harnesses act as highly transferable reasoning structures, consistently boosting diverse policy models.

RynnValue 是一个用于机器人操控的开源价值基础模型，用时间距离（即从观测到语言指定目标的有向 cost-to-go）替代任务内部锚点，将时间距离确立为通用机器人策略的可扩展监督目标和实用奖励接口。RynnValue, an open-source value foundation model for robotic manipulation that replaces task-internal anchors with temporal distance, the directed cost-to-go from an observation to the language-specified goal, establishes temporal distance as a scalable supervision target and practical reward interface for generalist robot policies.

我们提出 Ouroboros，一个自进化的 Agent harness，其工具、提示、上下文组装与核心实现通过已评审的 commit 持续改进，并成为后续工作的运行时。核心演化以两种模式推进：在递归自由演化中，改进本身就是任务，完成一个演化周期即可调度下一周期；在经验驱动核心演化中，常规工作和社交交互暴露的 bug、粗糙之处及低效上下文构造会引发已评审的结构变更。在 Terminal-Bench 2.1 上，Opus 5 运行取得 86.74% 的得分，为该基准报告的最佳结果。We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits that become the runtime for later work. Core evolution proceeds in two modes. In recursive free evolution, improvement is itself a task, and completing one evolution cycle can schedule the next. In experience-driven core evolution, ordinary work and social interaction expose bugs, rough edges, and inefficient context construction that lead to reviewed structural changes. On Terminal-Bench 2.1, an Opus 5 run scores 86.74%, the best result reporte

本文提出 OasisKV，一种以显存为中心的 LLM 推理系统设计，通过在 LLM 解码期间将完整 KV-cache 存储与 HBM 解耦来缓解 HBM 容量压力，并观察到未来重要 token 可借助推测解码（SD）所起草的前瞻 token 被提前准确预测。OasisKV is presented, a memory-centric LLM inference system design that alleviates HBM capacity pressure by decoupling full KV-cache storage from HBM during LLM decoding and observes that future important tokens can be predicted accurately in advance using lookahead tokens drafted by speculative decoding (SD).

本文提出 Agent Memory Distillation (AMD)，一个无需训练、通过分层记忆将结构化知识从大型教师智能体迁移到小型学生智能体的框架，一致优于现有基于记忆的基线方法。Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory, is proposed, which consistently outperforms existing memory-based baselines.

Steerling-8B 在与训练计算量多 2–16 倍的开源同侪模型对比中仍保持竞争力，表明存在一种不同的可扩展范式：可解释性可以被设计进训练过程中，并随规模放大而提升。Steerling-8B remains competitive with open peer models trained on substantially 2-16x more compute, suggesting a different scaling paradigm: interpretability can be designed into training, and it improves with scale.

本文提出 Factorized Hypothesis Search (FHS)，在多个命名语义维度上维护多个部分解释，以支持结构化查询渲染、多假设检索以及维度级候选验证。This work proposes Factorized Hypothesis Search (FHS), which maintains multiple partial interpretations over named semantic dimensions that support structured query rendering, multi-hypothesis retrieval, and dimension-level candidate verification.

一个三阶段框架，用于减少建议编辑与当前图像之间的视觉不一致性，并将推荐 CTR、图像带走率以及用户平均对话轮次分别显著提升 39.90%（所有 p<0.05）。A three-stage framework to reduce visual inconsistencies between suggested edits and the current image, which significantly improves recommendation CTR, image take-away rate, and average conversation turns per user by 39.90% (all p<0.05).