论文

提出 Stratum，一种统一系统基础设施，在 agentic pipeline 搜索期间将 pipeline 执行与规划和推理解耦，无缝集成现有 Python 库，将批量 pipeline 编译为优化的执行图，并通过基于 Rust 的新型 runtime 等异构后端高效执行。Stratum is proposed, a unified system infrastructure that decouples pipeline execution from planning and reasoning during agentic pipeline search, and integrates seamlessly with existing Python libraries, compiles batches of pipelines into optimized execution graphs, and efficiently executes them across heterogeneous backends, including a novel Rust-based runtime.

HERA 是一个分层框架，联合演化多 Agent 编排与角色特定的 Agent prompt，在多 Agent RAG 中实现针对性的、角色条件化的改进，并提供角色感知的 prompt 演化。HERA is a hierarchical framework that jointly evolves multi-agent orchestration and role-specific agent prompts, enabling targeted, role-conditioned improvements in multi-agent Retrieval-Augmented Generation and Role-Aware Prompt Evolution.

本文认为 UQ 研究必须转向面向交互式 Agent 的现实场景，需要一套面向 Agent 的新原则性框架，并首次提出了能涵盖现有 UQ 设置的 Agent UQ 通用形式化方法。This paper argues that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed, and presents the first general formulation of agent UQ that subsumes broad classes of existing UQ setups.

文中认为，编码 Agent 已跨越能力门槛，传统的人工 code review 已不再是软件质量流水线的必要环节。It is argued that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline.

该方法通过在现有企业搜索基础设施之上叠加一层轻量级 harness，为推理型 LLM 配备 search、find、open、summarize 等工具，使其能够自主地迭代检索信息、跨文档导航并分析证据，从而降低对单一环节的过度依赖。The approach reduces this overdependence by layering a lightweight harness on top of existing enterprise search infrastructure, equipping a reasoning LLM with search, find, open, and summarize tools enabling the model to iteratively retrieve information, navigate within documents, and analyze evidence autonomously.

本文提出了一种实证方法，用于研究基于 GPU 的 LLM 推理服务系统中的 software aging 问题，并提供了一个可复现的框架，开辟了 software aging 与 software rejuvenation 与 LLM serving 交叉方向的研究。This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems and provides a reproducible framework that opens a research direction at the intersection of the software aging and rejuvenation and LLM serving communities.

分析表明，鲁棒的长期记忆（Long-Term Memory）安全无法仅在 retrieval 或执行阶段后置加固，而必须在最初就以存储阶段的溯源、版本化与策略感知的 retention 为基础进行锚定。This analysis indicates that robust Long-Term Memory security cannot be retrofitted at retrieval or execution time alone, but must be anchored in storage-time provenance, versioning, and policy-aware retention from the outset.

本文推出 STEP3-VL-10B，一个面向"紧凑效率与前沿级多模态智能"权衡的轻量级开源基础模型，并发布完整模型套件，为社区提供强大、高效且可复现的 baseline。STEP3-VL-10B is presented, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence, and the full model suite is released to provide the community with a powerful, efficient, and reproducible baseline.

本文概述了第二届 MAGMaR（Multimodal Retrieval 驱动的多模态增强生成）研讨会共享任务的成果，参赛系统聚焦于视频检索，或在给定检索视频的基础上进行有依据的文章生成。This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR), where participants submitted systems focused on either video retrieval or grounded generation of articles given retrieved videos.

本文提出首个将计算与 GPU 显存约束显式纳入 LLM 推理分析的排队论框架，并推导了严格的稳定性与不稳定性条件，用以判定 LLM 推理服务能否在持续到达的请求下避免队列无界增长。This paper introduces the first queueing-theoretic framework that explicitly incorporates both computation and GPU memory constraints into the analysis of LLM inference, and derives rigorous stability and instability conditions that determine whether an LLM inference service can sustain incoming demand without unbounded queue growth.

提出 SIFT：Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance，离线处理文档并提取每个文档中高分注意力的细粒度位置，以两个紧凑的位向量存储这些高分位置。SIFT: Selective-Index For Fast Compute of RAG Prefill by Exploiting Attention Invariance is proposed, which processes documents offline and extracts fine-grained locations of high attention scores for each document and stores locations of high scores in the form of two compact bit vectors.

本文围绕形式化框架与 Agent 生命周期，对面向 LLM-based Agent 的文本世界模型进行系统性综述，旨在整合这一快速发展的方向、厘清其设计空间，并指出未来研究的开放挑战。This work systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle, and aims to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.

本文提出 Beyond Rows to Reasoning (BRTR)，一种面向电子表格理解的多模态 Agentic 框架，将单轮检索替换为迭代式工具调用循环，支持从复杂分析到结构化编辑的端到端 Excel 工作流。This work introduces Beyond Rows to Reasoning (BRTR), a multimodal agentic framework for spreadsheet understanding that replaces single-pass retrieval with an iterative tool-calling loop, supporting end-to-end Excel workflows from complex analysis to structured editing.

介绍 Gemini for Google (GfG)，一款面向 Google 内部软件工程生态的 Gemini 专用适配版本，涵盖从构建万亿 token 的专有数据集到采用可缓解灾难性遗忘的中段训练策略的完整过程。G Gemini for Google (GfG)}, an adaptation of Gemini specialized for Google's internal software engineering ecosystem, is introduced, from curating a trillion-token proprietary dataset to implementing a mid-training strategy that mitigates catastrophic forgetting.

介绍 Albireo，一种并行推理系统：通过调度与 I/O 与计算及序列并行采样的重叠来压缩不可扩展部分，从而提高可达到的 TP 度 t，且不改变模型架构。Albireo is presented, a parallel inference system that raises the attainable TP degree t by shrinking the non-scalable portion via overlap of scheduling and I/O with compute and sequence-parallel sampling, without changing model architectures.

介绍 Corpus2Skill：离线将文档语料蒸馏为分层 Skill 目录，服务时由 LLM Agent 自顶向下导航——从全局概览逐层深入到更细粒度的摘要再到具体文档，并在分支无效时回溯。Corpus2Skill is presented, which distills a document corpus offline into a hierarchical skill directory and lets an LLM agent navigate it at serve time, drilling from a bird's-eye view through progressively finer summaries down to documents, and backtracking when a branch is unproductive.

对八款主流开源 MLOps 框架的实践使用与功能增强需求进行调查，结果显示 MLOps 框架很少被直接开箱即用，也较少集成进 GitHub Workflows，开发者更多通过其 API 在项目中实现自定义功能。Investigating the practical use and desired feature enhancements of eight popular open-source MLOps frameworks indicates that users mainly ask for enhancements to core features of the frameworks, but also better API exposure and CI/CD integration.

本研究对基于RAG的系统与工具增强的LLM Agent进行系统对比，特别是在Agent仅可访问基础关键词搜索工具的条件下，评估二者的检索机制与响应质量。This study conducted a systematic comparison between RAG-based systems and tool-augmented LLM agents, specifically evaluating their retrieval mechanisms and response quality when the agent only has access to basic keyword search tools.

本文主张将这些多样化的功能统一在单一抽象与一组通用计算原语之下，使其足够强大以涵盖现有用例并支持新用例。This paper argues for unifying these diverse functionalities under a single abstraction and a common set of computational primitives, powerful enough to encompass existing use cases and to support new ones.

本文提出FROAV（Framework for RAG Observation and Agent Verification），一个通过提供即插即用架构——结合可视化工作流编排、综合性评估框架与可扩展Python集成——来降低LLM Agent研究门槛的开源研究平台。FROAV (Framework for RAG Observation and Agent Verification), an open-source research platform that democratizes LLM agent research by providing a plug-and-play architecture combining visual workflow orchestration, a comprehensive evaluation framework, and extensible Python integration, is presented.

本文分析了六个公共注册表中共计67,057个服务器，识别出可导致服务器劫持与调用操控的普遍隐患，并实现了MCPInspect——一款集成前分析工具，可检测误导性的工具元数据与可利用的代码漏洞。This paper analyzes 67,057 servers across six public registries and identifies widespread conditions enabling server hijacking and invocation manipulation, and implements MCPInspect, a pre-integration analysis tool that detects misleading tool metadata and exploitable code vulnerabilities.

本文提出TSseek，一个面向分布式时间序列数据集的正则表达式驱动搜索框架，并论证传统近似技术及其索引结构因无法作用于正则表达式查询构造而不适用于此类查询。This work proposes TSseek, a regular-expression-powered search framework for distributed time series datasets, and shows that conventional approximation techniques and their index structures are ill-suited for such queries because they cannot operate on regular-expression query constructs.

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.

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.

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.

提出 TRUSTMARGIN，一种免训练、即插即用的仲裁层，利用模型自身的似然对两个候选进行打分，在不微调、无需外部评判或额外生成的情况下，在直接回答与 RAG 之间进行选择。TRUSTMARGIN is proposed, a training-free, plug-and-play arbitration layer that scores the two existing candidates with the model's own likelihoods and selects between Direct and RAG without fine-tuning, external judges, or additional generation.

本工作开发了一个 SOTA 的 paged attention kernel，完全基于领域特定即时编译语言 Triton 构建，在 NVIDIA 与 AMD GPU 上均达到 SOTA 性能。This work develops a state-of-the-art paged attention kernel that builds exclusively on the domain-specific just-in-time compiled language Triton to achieve state-of-the-art performance on both NVIDIA and AMD GPUs.

本工作提出一种新颖的混合精度 PTQ 策略，直接最小化整个模型的全局误差传播，而非孤立地处理逐层误差，并开发了一种新颖的联合优化方法，在统一搜索空间中同时学习结构化剪枝决策与混合精度量化策略。This work proposes a novel mixed-precision PTQ strategy that directly minimizes global error propagation across the entire model, rather than isolating layer-wise errors, and develops a novel joint optimization approach that simultaneously learns structural pruning decisions and mixed-precision quantization policies within a unified search space.

本工作提出 QBugLM，一个多 Agent 框架，可自动化量子软件调试流水线，覆盖基于分类法的缺陷注入、基于 LLM 的检测与修复，直至基于仿真的验证，框架无关地支持 OpenQASM 3.0 程序。This work proposes QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs.

PLENA 是一个软硬件协同设计的系统，采用三条核心优化路径，具备新颖的扁平化 systolic-array 架构以及支持非对称量化方案的高效计算与存储单元（路径 2）。PLENA is a hardware-software codesigned system that applies three core optimization pathways that features a novel flattened systolic-array architecture and efficient compute and memory units that support an asymmetric quantization scheme (Pathway 2).

本工作将 embedding 缩放作为正交于稀疏度缩放的强有力维度加以探索，并推出 LongCat-Flash-Lite，一个从零训练的 68.5B 参数、约 30 亿激活参数的模型，不仅超越参数等量级的 MoE 基线，还对同规模现有模型展现出卓越竞争力。This work explores embedding scaling as a potent, orthogonal dimension for scaling sparsity and introduces LongCat-Flash-Lite, a 68.5B parameter model with ~3B activated trained from scratch that not only surpasses parameter-equivalent MoE baselines but also exhibits exceptional competitiveness against existing models of comparable scale.

ViT-5 的设计与当代基础模型实践保持一致，可作为对 vanilla ViT 的直接替换升级方案，适用于 2020 年代中期的视觉骨干网络，并为生成建模提供更强大的骨干。With a design aligned with contemporary foundation-model practices, ViT-5 offers a simple drop-in upgrade over vanilla ViT for mid-2020s vision backbones and serves as a stronger backbone for generative modeling.