论文

近期大语言模型（LLM）已能作为编码 Agent，根据自然语言请求构建完整游戏。游戏开发尤为严苛，因为程序逻辑、视觉与音频内容、界面、交互和可玩性必须在同一可执行制品中协同工作。因此衡量该能力需要同时评测游戏产品与开发过程。现有基准通常通过评估最终制品或孤立的开发阶段来评测 LLM 的游戏开发能力。我们对完整人机协作开发过程的分析Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especially demanding because program logic, visual and audio content, interfaces, interaction and playability must function together in one executable artifact. Measuring this capability therefore requires evaluation of both game product and the development process. Existing benchmarks often assess the game development capabilities of LLMs by evaluating the final artifact or an isolated development stage. Our analysis of complete human-agent development

尽管文本到 3D 生成进展迅速，在低推理成本下实现高几何保真度仍具挑战。现有文本到 3D 方法要么自回归地解码离散形状 token，要么通过扩散或流模型迭代优化全局 3D 表示。然而，自回归解码是顺序执行的且无法修正错误，而扩散与流匹配模型反复处理完整表示，使高质量生成成本日益高昂。本文提出 Block3D，一种块级扩散框架，将离散 sWhile text-to-3D generation has advanced rapidly, achieving high geometric fidelity at low inference cost remains challenging. Existing text-to-3D methods either decode discrete shape tokens autoregressively or iteratively refine global 3D representations with diffusion or flow models. However, autoregressive decoding is sequential and cannot revise errors, whereas diffusion and flow-matching models repeatedly process the full representation, making high-quality generation increasingly expensive. In this paper, we propose Block3D, a block-wise diffusion framework that partitions the discrete s

现有图像编辑框架主要沿用 text-to-image 扩散模型的训练范式。然而将该范式扩展到图像编辑时暴露出两个固有差异：一是对编辑概念粒度的关注不足，二是稀疏监督信号导致的训练低效。为应对这些问题，我们建立了一个包含超过 1,000 种细粒度编辑概念的综合分层分类体系，并构建了 ConceptEdit-12M——通过改进的合成框架生成的包含 1,200 万高质量编辑对的超大规模数据集。该 library-drExisting image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inherent discrepancies, specifically, the insufficient attention to edit concept granularity and the training inefficiency caused by sparse supervision signals. To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high-quality editing pairs via an improved synthesis framework. This library-dr

开放式真实交互允许多种有效行为：Agent 可直接回答、请求澄清、提供进度更新，或在执行前进行确认。这种灵活性打破了基于分组的 RL 的核心假设：同一分组内对比的 rollout 不再保证行为可比。因此奖励模型对交互风格的偏好可能扭曲相对优势，使优化偏向奖励偏好的行为而非情境适配的行为。我们将其形式化为奖励公平性问题，并提出 ARC（Advantage RegularizatiOpen-ended real-world interaction admits multiple valid behaviors: an agent may answer directly, ask for clarification, provide progress updates, or confirm before acting. This flexibility breaks a core assumption behind group-based RL: rollouts compared within a group are no longer guaranteed to be behaviorally comparable. As a result, reward-model preferences over interaction style can distort relative advantages and steer optimization toward reward-preferred behaviors rather than context-appropriate ones. We formalize this as a reward fairness problem and propose ARC (Advantage Regularizati

一条安全规则与一段情景日志在同一 AI Agent 上下文中争夺 token。当预算溢出时，二者以相同速率被压缩；但只有规则需要精确措辞才能保持可执行性。在 20 种生产环境 Agent 配置下，Claude Code 基于 Sonnet 4.6 的 /compact 提示在一轮压缩后保留 53% 的安全规则，五轮后仅保留 10%。我们将此现象命名为"压缩悬崖"（Compaction Cliff）。我们提出 Knowledge Triage 框架，通过对 Agent 知识库的每一行按类型分类，并为每类配置独立的保留策略来解决该问题。三种确定性 operatA safety rule and an episodic log compete for the same tokens in an AI agent's context. When the budget overflows, both are summarized at the same rate; only the rule needs exact wording to remain enforceable. On 20 production agent configurations, Claude Code's /compact prompt on Sonnet 4.6 preserves 53\% of safety rules after one compaction round and 10\% after five. We name this the Compaction Cliff. We address it with Knowledge Triage, a framework that classifies each line of an agent's knowledge base by type and routes each type through its own retention policy. Three deterministic operat

我们提出了一种面向 TianoCore 开源 UEFI 固件开发生态中缺陷分诊的新方法。该集成方法名为 TianoForge，部署了人工智能（特别是机器学习）领域的前沿技术，以实现自动化的缺陷分诊，包括无效缺陷报告检测、重复缺陷报告检测、缺陷报告优先级排序以及缺陷报告分配。我们使用多种 Generative Pretrained Transformer (GPT) Large Language Models (LLMs)，结合与不结合 Retrieval Augmented Generation (RAG)，来完成这些任务。鉴于缺陷分诊在We propose a novel approach to bug triage in the TianoCore open-source UEFI firmware development ecosystem. This integrated approach, called TianoForge, deploys the state of the art in artificial intelligence, specifically machine learning, to enable automated bug triage. This includes invalid bug report detection, duplicate bug report detection, bug report prioritization, and bug report assignment. We use various Generative Pretrained Transformer (GPT) Large Language Models (LLMs) with and without Retrieval Augmented Generation (RAG) to automate these tasks. Given the crucial role of bug tria

随着 Retrieval-Augmented Generation (RAG) 向多样化组合生成方向发展，它受到两个关键瓶颈的阻碍：对证据利用的度量有缺陷，以及上下文预算分配欠优。我们依次解决这两个问题。为解决度量问题，我们揭示了一种普遍的"诊断幻觉"：标准相关性代理在难负例上表现糟糕。我们用一种高效的因果留一探针来取代它们，该探针能够准确隔离生成式依赖关系，并形式化地校准 LLM 注意力的结构性稀释。为解决分配问题，我们部署该因果探针As Retrieval-Augmented Generation (RAG) shifts toward diverse portfolio generation, it is stymied by two critical bottlenecks: flawed measurement of evidence utilization, and suboptimal context budget allocation. We resolve both sequentially. To resolve measurement, we expose a pervasive ``diagnostic illusion'': standard relevance proxies fail catastrophically on hard negatives. We replace them with an efficient causal leave-one-out probe that accurately isolates generative reliance and formally calibrates the structural dilution of LLM attention. To resolve allocation, we deploy this causal p

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.

真实工业场景中的用户表示学习通常通过增加用户数量、行为序列长度和模型规模来扩展。然而现有方法面临两个挑战：(i) 十亿级容量下原始数据扩展的瓶颈，因为随着更大规模原始文本用户行为输入的增加，性能增益呈现递减趋势，这可以通过 tokenization 缓解；(ii) 缺乏对 tokenization 配置应如何随数据规模扩展的定量分析。本报告中，我们提出 User Behavioral Densing Law 来刻画定量关系User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods face two challenges: (i) Bottleneck for raw data scaling at billion-scale capacity, as performance exhibit diminishing performance gains with larger-scale raw text user behavioral input, which can be mitigated by tokenization. (ii) Lack of quantitative analysis of how tokenization configurations should scale with data size. In this report, we propose User Behavioral Densing Law for characterizing the quantitative rela

大语言模型日益被期望执行复杂工作流，其成功依赖于维持相互依赖的约束并产出满足严格端到端验证的产物。然而成功的执行经验通常在单次运行后就丢失了，迫使后续模型从头重新发现策略和失败模式。我们研究能否通过 EvoMap 将此类经验外部化并复用，其中验证器确认的执行轨迹被整合为结构化的 Gene。为评估该设定，我们引入长工作流基准Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet successful execution experience is typically lost after a single run, forcing subsequent models to rediscover strategies and failure modes from scratch. We study whether such experience can instead be externalized and reused through EvoMap, where verifier-confirmed execution trajectories are consolidated into structured Gene. To evaluate this setting, we introduce the Long-Workflow B

自主研究系统执行长研究工作流的能力日益增强，但仅靠自动化并不能确保所得流程保持科学严谨性。我们提出 AutoResearch，一个两阶段系统，将 Idea Generation 与 Idea Execution 相连，以同时解决研究想法如何形成以及如何通过实验可靠建立的问题。在 Idea Generation 中，AutoResearch 持续整合新出现的研究信号与累积的领域知识，识别可迁移的机理洞察，并采用多模型生成与跨模型Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that connects Idea Generation with Idea Execution to address both how research ideas are formed and how they are reliably established through experimentation. In Idea Generation, AutoResearch continuously integrates emerging research signals with accumulated domain knowledge, identifies transferable mechanistic insights, and uses multi-model generation and cross-r

低成本服务大语言模型日益意味着交付既经过结构压缩至部分参数、又量化到 4 bit 的模型。这两个步骤叠加后会显著降低推理、数学、代码以及长上下文能力，因此在部署前需要一个恢复（即修复）阶段。默认方案 quantization-aware training (QAT) 将压缩并量化后的模型重新拟合到硬标签；在我们的流水线中它收敛缓慢，且在峰值之后出现性能崩塌。我们转而采用 Quantization-Aware Healing (QAH)。由于结构压缩后的模型从未Serving large language models cheaply increasingly means shipping models that are both structurally compressed to a fraction of their parameters and quantized to 4 bits. Together these steps degrade reasoning, mathematics, coding, and long-context behavior enough to require a recovery, or healing, stage before deployment. The default recipe, quantization-aware training (QAT), re-fits the compressed, quantized model to hard labels; in our pipeline it converged slowly and collapsed past its peak. We adopted Quantization-Aware Healing (QAH) instead. Because a structurally compressed model is neve

深度人脸识别 (FR) 模型已达到近乎饱和的精度，但仍缺乏透明度：从业者无法追问某个相似度分数究竟依赖了哪些语义属性。EXPL-FR 在 FR 模型自身的嵌入空间内给出答案。一个轻量适配器将 vision-language model (VLM) 的图像编码器与冻结的 FR 空间对齐，仅基于人脸图像训练，从不基于文本。由于 VLM 的编码器共享同一空间，同一适配器同样适用于文本编码器，从而无需额外成本即可将 22 个类别中的 978 条属性提示（也可扩展）转化为 FR 空间锚点。我们并未假设这种迁移Deep face recognition (FR) models reach near-saturated accuracy but remain opaque: a practitioner cannot ask which semantic attributes a similarity score relied upon. EXPL-FR answers this inside the FR model's own embedding space. A lightweight adapter aligns a vision-language model's (VLM) image encoder with the frozen FR space, trained on face images alone and never on text. Because the VLM's encoders share one space, the same adapter applies to the text encoder, turning 978 attribute prompts in 22 categories, also extendable, into FR-space anchors at no extra cost. We do not assume this tra

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.

GLM-5 在真实编码任务中展现出前所未有的能力，在端到端软件工程挑战的处理上超越既有基线，并提出了新颖的异步 Agent RL 算法，进一步提升了 RL 质量。GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges and proposing novel asynchronous agent RL algorithms that further improve RL quality.

本工作借鉴线性模型的 state space model（SSM）视角，提出三项核心方法改进并组合形成更具表达力的递推结构：源自 SSM 离散化的递推式、用于更丰富状态追踪的复数值状态更新规则，以及在不增加 decode 延迟前提下提升模型性能的多输入多输出（MIMO）建模。This work introduces three core methodological improvements inspired by the state space model (SSM) viewpoint of linear models that combine to form a more expressive recurrence derived from SSM discretization, a complex-valued state update rule that enables richer state tracking, and a multi-input, multi-output (MIMO) formulation for better model performance without increasing decode latency.

Nemotron 3 Super 是 Nemotron 3 系列中首个采用 NVFP4 进行预训练的模型，借助 LatentMoE（一种同时优化精度 per FLOP 与精度 per parameter 的新型 Mixture-of-Experts 架构），并集成 MTP 层以通过原生 speculative decoding 加速推理。Nemotron 3 Super is the first model in the Nemotron 3 family to be pre-trained in NVFP4, leverage LatentMoE, a new Mixture-of-Experts architecture that optimizes for both accuracy per FLOP and accuracy per parameter, and include MTP layers for inference acceleration through native speculative decoding.

基于实证对 "Enhanced" 与 "Agentic" RAG 范式进行评估，为真实场景中选取最有效的 RAG 设计（兼顾性能与成本）提供指导。An empirically driven evaluation of the "Enhanced" and "Agentic" RAG paradigms is conducted, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.

本教程综合了推动这些汇聚方向的算法、系统与设计原则，为数据科学与数据挖掘研究者提供统一视角，涵盖将 LLM、图数据管理、图挖掘、图 ML 与 agentic 计算融合到下一代 graph-native AI 系统中。This tutorial synthesizes the algorithms, systems, and design principles driving these converging directions, offering data science and data mining researchers a unified perspective on integrating LLMs, graph data management, graph mining, graph ML, and agentic computation into next-generation graph-native AI systems.

本文提出一种分层隔离架构，结合策略感知的 ingestion、retrieval-time gating 与共享推理，并通过服务端 agentic 编排加以执行，在为多租户隔离提供天然强制点的同时，允许客户端框架保留对 agent 组合与延迟敏感操作的控制权。A layered isolation architecture combining policy-aware ingestion, retrieval-time gating, and shared inference, enforced through server-side agentic orchestration is introduced, creating natural enforcement points for multitenant isolation while allowing client-side frameworks to retain control over agent composition and latency-sensitive operations.

本文提出 LoRAFusion，一种面向 LLM 的高效 LoRA 微调系统，可消除不必要的内存访问，在不付出重算或同步代价的前提下保持 compute-bound GEMM 的性能，并引入面向多任务微调的自适应批处理算法。LoRAFusion is introduced, an efficient LoRA fine-tuning system for LLMs that eliminates unnecessary memory accesses and preserves the performance of compute-bound GEMMs without incurring the cost of recomputation or synchronization and introduces an adaptive batching algorithm for multi-job fine-tuning.

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.

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.

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.

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.

本文提出通过让 evaluator 与解决方案协同进化来自动化 evaluator 的设计，并证明突破 evaluation 瓶颈可释放 ADRS 的潜力，为下一代数据系统生成高度优化、可部署的代码。This work proposes automating the design of evaluators by co-evolving them with the solutions, demonstrating that addressing the evaluation bottleneck unlocks the potential of ADRS to generate highly optimized, deployable code for next-generation data systems.

本文为 LLM serving 中的 SD（投机解码）提出了一种简单且可解释的 latency 模型，能够准确刻画实际观测到的 latency，解释为何加速比常随服务器负载上升而下降，并系统刻画了 draft length、acceptance rate 以及 verifier 与 drafter 规模在不同 serving 条件下对 latency 的影响。A simple and interpretable latency model for SD in LLM serving is developed that accurately describes observed latency, explains why speedups often diminish as server load increases, and characterizes how draft length, acceptance rate, and verifier-drafter size shape latency across serving conditions are characterized.

Tutti 是一种高效的 SSD-backed KV caching 方案，将 CPU 从 HBM 与 SSD 之间的关键数据与 I/O 控制路径中彻底移除，在提供近乎无限容量的同时，实现了与 DRAM-backed LMCache 几乎相当的 inference 性能。Tutti is an efficient SSD-backed KV caching solution that eliminates CPU intervention from the critical data and I/O control paths between HBM and SSDs, and achieves nearly the same inference performance as DRAM-backed LMCache, while providing almost infinite capacity.

这些结果表明，Kueue、DAS 与 GAIE 等互补组件构成了一个高性能的协同平台，证明了 Kubernetes 能够作为承载高要求 GenAI 工作负载的统一底座。These findings illustrate that these complementary components (Kueue, DAS, and GAIE) form a cohesive, high-performance platform, proving Kubernetes' capability to serve as a unified foundation for demanding GenAI workloads.