论文

ReViV 是首个用于整体第一人称 4D 重建的统一框架，能够从单个单目 RGB 视频中同时提取观察者与视角动态，在整体自身体、手部与注视重建以及相机跟踪方面达到 SOTA 精度与效率，同时保持极具竞争力的第一人称深度估计能力。ReViV is the first unified framework for holistic egocentric 4D reconstruction that extracts both viewer and view dynamics from a single monocular RGB video and achieves state-of-the-art accuracy and efficiency across holistic ego-body, hand, and gaze reconstruction, camera tracking, while maintaining highly competitive egocentric depth estimation.

本工作通过 teacher forcing 与 ODE 蒸馏，将一个双向动作条件教师模型逐步蒸馏为因果学生模型，并提出 LongForcing，将学生模型的长时间自展开与扩展时域教师模型对齐，从而缓解累积的分布漂移与自回归漂移。This work progressively distill a bidirectional action-conditioned teacher into a causal student through teacher forcing and ODE distillation, and introduces LongForcing to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift.

本文是面向业务流程中长时间运行、有状态、多步生成式 AI 系统的基于图的工作流路径实践指南，并通过三个可执行示例展示类型化状态、条件路由、确定性工具、重试、中断、检查点与 trace 如何协同工作。This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes and presents three executable recipes to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together.

在该研究的评估协议下，部分 AI 生成回答获得了高于可用参考答案的评分，尤其当其包含准确且相关的支撑细节时尤为明显。Under the study's evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting details, particularly when they contained accurate and relevant supporting details.

本文提出 RF-Agent，通过多 Agent 的 Question-Thinking-Solution-Answer 流水线，基于教材驱动的知识蒸馏来弥补 RF 领域专用推理的空白，为面向 LLM 辅助 RF 电路设计的未来工作提供了可复用的基础。RF-Agent is presented, which addresses the gap in domain-specific RF reasoning through textbook-driven knowledge distillation through a multi-agent Question-Thinking-Solution-Answer pipeline and provides a reusable foundation for future work on LLM-aided RF circuit design.

这些发现表明，即便长上下文评估正从简单检索转向复杂推理，对相关证据的准确 grounding 仍是一项不可或缺且仍有大幅提升空间的能力。These findings suggest that, even as long-context evaluation shifts from simple retrieval toward complex reasoning, accurate grounding in relevant evidence remains an indispensable capability with substantial room for improvement.

本文提出 HACO，一种运行时控制方案，将每次角色请求视为在候选 agent 实例上的可靠性约束选择问题，每个候选实例耦合了角色类型、LLM 与具体执行环境。HACO is proposed, a runtime control scheme that treats each role request as a reliability-constrained selection problem over candidate agent instances, each coupling a role type, an LLM, and a concrete execution environment.

DeepDebug 在两个测试的开源权重 backbone 上均取得了所评估方法中最高的严格归因准确率，在 qwen3.5-9b 上达到 28.8% 的精确 agent 与步骤准确率，而最强的单遍 baseline 为 21.7%。DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline.

EduPanel 是一个基于评分量表、以学习者为条件的 LLM 评判器，通过在多个专用 agent 间分解评估流程，对教学质量的各个方面产出可解释的评估结果，其可靠性与中等水平的人类专家相当。E EduPanel, a rubric-grounded, learner-conditioned LLM judge that decomposes evaluation across specialized agents to produce interpretable assessments for different aspects of teaching quality, achieves reliability comparable to a median human expert.

本文提出 Verbatimize 这一新任务，能够以高质量规范化逐字转录对语音语料库进行可扩展的创建与扩充，并通过有监督的 cross-attention 微调，将不流畅语音上的词级时间戳表现提升至超过强制对齐 baseline。Verbatimize is proposed, a new task enabling scalable creation and enrichment of speech corpora with high-quality canonical verbatim transcriptions and supervised cross-attention fine-tuning that improves word-level timestamps on disfluent speech beyond forced-alignment baselines.

本文研究 SkewAdam——一种基于以下观察构建的优化器：MoE 的三类参数群体在规模与梯度统计上差异足够大，不应共享相同的状态；研究表明优化器状态的存放位置至少与状态容量同等重要。SkewAdam, an optimizer built on the observation that the three parameter populations of an MoE differ enough in size and gradient statistics that they should not receive the same state, is studied, suggesting where optimizer state lives matters at least as much as how much of it there is.

本文描述了 TensorFlow 接口及 Google 构建的该接口实现，已被用于开展研究，并在计算机科学及其他十余个领域中将机器学习系统部署至生产环境。The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.

本文推出参数规模从 7B 到 65B 的基础语言模型集合 LLaMA，并证明完全使用公开数据集即可训练出 SOTA 模型，无需依赖专有或不可获取的数据。LLaMA, a collection of foundation language models ranging from 7B to 65B parameters, is introduced and it is shown that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.

研究发现，在上述多个维度上进行的指令微调可显著提升多种模型类别（PaLM、T5、U-PaLM）、多种提示设定以及多种评测基准（MMLU、BBH、TyDiQA、MGSM、开放式生成）上的表现。It is found that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups, and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).

通过从概率分布的动态 nucleus 中采样文本，可在有效截断不可靠分布尾部的同时保持多样性，使生成文本更接近人类文本质量，在不牺牲流畅性与连贯性的前提下提升多样性。By sampling text from the dynamic nucleus of the probability distribution, which allows for diversity while effectively truncating the less reliable tail of the distribution, the resulting text better demonstrates the quality of human text, yielding enhanced diversity without sacrificing fluency and coherence.

本文指出基于 Transformer 的次二次时间模型的关键缺陷在于无法执行基于内容的推理，并将选择性 SSM 集成到不包含注意力乃至 MLP 块的简化端到端神经网络架构（Mamba）中。This work identifies that a key weakness of subquadratic-time models based on Transformer architecture is their inability to perform content-based reasoning, and integrates selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba).

本文为两部分组成的专题论文，介绍物理信息神经网络——一类在训练求解监督学习任务时遵循由一般非线性偏微分方程所描述的物理定律的网络；并展示如何利用这些网络推断偏微分方程的解，以及获得对所有输入坐标和自由参数完全可微的物理信息代理模型。This two part treatise introduces physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations and demonstrates how these networks can be used to infer solutions topartial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters.

本文认为，可解释 AI 研究总体上有助于推动 AI/ML 在医疗领域的落地，并特别有助于增强透明性与信任。It is argued that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitates transparency and trust.

本文从技术演进的角度，对这一快速发展的研究领域进行了广泛综述，跨越超过四分之一世纪的时间跨度（从 1990 年代到 2022 年）。This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century’s time (from the 1990s to 2022).

一个能够将任意自然语言任务轻松映射为人类可读 prompt 形式的系统，并在覆盖多种任务的多任务混合数据上对预训练 encoder-decoder 模型进行微调。A system for easily mapping any natural language tasks into a human-readable prompted form and fine-tune a pretrained encoder-decoder model on this multitask mixture covering a wide variety of tasks.

研究结果表明，文档表示不应被视为检索开始前一次性的固定预处理选择，而应作为一个明确的优化目标。The results suggest that document representation should not be treated as a fixed preprocessing choice made before retrieval begins, but as an explicit optimization target.

本综述聚焦于单图与多格视觉作品中的幽默理解，同时将幽默生成视为新兴的下游前沿方向，并围绕多模态对齐、证据 grounded 推理与可控生成，对基准设计、评估协议与建模范式进行系统综述。This survey focuses on visual humor understanding in single-image and multi-panel artifacts, while treating humor generation as an emerging downstream frontier, and synthesizes benchmark design, evaluation protocols, and modeling paradigms based on multimodal alignment, evidence-grounded reasoning, and controlled generation.

本文提出 IteraSim RAG，一个面向自动化 OpenFOAM 算例生成的 RAG 软件后端，围绕三大局限构建：求解器选择、湍流闭合、边界条件与有限体积默认值。IteraSim RAG is presented, a retrieval-augmented software back-end for automated OpenFOAM case generation built around three limitations: solver selection, turbulence closures, boundary conditions and finite-volume defaults.

FinMMEval 2026 Task 2 围绕多语言证据评估金融领域的短答问答，采用文档 RAG、跨语言证据处理、结构化提示、答案压缩与验证策略。FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence over multilingual evidence using document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.

Self Gradient Forcing（SGF）是一种两阶段训练策略，在原生自回归训练目标内恢复缺失的"记忆写入"监督信号，通过对未来视频 latent 的损失来训练模型将上下文编码为更有效的因果记忆。Self Gradient Forcing (SGF), a two-pass training strategy that restores this missing memory-writing supervision within the native autoregressive training objective, using losses on future video latents to train the model to encode context into more effective causal memory.

本文提出 Trace，一个面向多领域视觉推理的、由分类体系引导的环境，其将任务构建分解为场景语法与可执行任务程序，将视觉呈现与答案计算解耦，并提供了广泛的程序化训练可迁移到生成任务分布之外的证据。Trace is introduced, a taxonomy-guided environment for multidomain visual reasoning that factorizes task construction into a scene grammar and an executable task program, separating visual realization from answer computation, providing evidence that broad procedural training can transfer beyond the generated task distributions.

该设计将 hypernetwork 的注入能力与目标模型的通用能力解耦，首次实现了对 hypernetwork 架构 scaling law 的严格研究，并提供了首个基于实证的 scaling law，用以指导大语言模型中面向事实推理的 hypernetwork 设计。The design decouples the hypernetwork's injection capacity from the target model's general capability, enabling, for the first time, a rigorous study of scaling laws for hypernetwork architectures, and provides the first empirically grounded scaling laws to guide hypernetworks for factual reasoning in large language models.

人类视觉是一个闭环：注视点不断被中间假设而非单一快照持续重定向。数十年的心理物理学与认知科学研究表明，主动观察对多种任务至关重要。当代多模态大语言模型 (MLLM) 是否进行主动观察，是一个现有视觉语言基准无法回答的经验问题。我们提出 ActiveVision，一个使 MLLM 主动观察可度量的基准，包含 3 个类别共 17 个任务，任务设计强制进行重复视觉感知……Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (MLLMs) exercise active observation is an empirical question that current vision-language benchmarks do not answer. We introduce ActiveVision, a benchmark that makes active observation measurable for MLLMs, comprising 17 tasks across 3 categories. Tasks are designed to force repeated visual perception

本文提出 \method，一种无需训练的稀疏注意力系统，可在多 GPU 序列并行下提升自适应稀疏注意力的分布式执行效率。This work presents \method, a training-free sparse-attention system that improves the distributed execution efficiency of adaptive sparse attention under multi-GPU sequence parallelism.

本文提出 Anchor-Align，通过两个目标增强 BC：Vision-Language Anchoring 从冻结 VLM 副本中蒸馏逐层表示以防止该漂移；Language-Action Alignment 将每个动作目标转换为离散的运动方向标签，并在同一机器人观测上联合训练语言与动作预测。Anchor-Align is proposed, which augments BC with two objectives: Vision-Language Anchoring distills layer-wise representations from a frozen VLM copy to prevent this drift, and Language-Action Alignment converts each action target into a discrete motion-direction label and jointly trains language and action prediction on the same robot observation.

提出 DocOps，一种确定性可验证的评估框架，基于分层分类法，将受真实实践启发的文档操作分解为原子维度与逐级递增的工作流复杂度，从而揭示 Agent 在维护全局文档一致性方面的能力边界。DocOps is introduced, a deterministically verifiable evaluation framework underpinned by a hierarchical taxonomy that deconstructs document operations inspired by real-world practices into atomic dimensions and escalating workflow complexities that exposes the capability boundaries of agents in maintaining global document consistency.

提出两项审计协议——grounding and truth 对比以及 swap to an independent evaluator，以及 RECAP（Readable Encodings via Co-trained Auxiliary Predictors），即与目标模型联合训练的线性头，用于保持指定内容的可解码性。Two audit protocols, the comparison of grounding and truth and the swap to an independent evaluator, and RECAP (Readable Encodings via Co-trained Auxiliary Predictors), linear heads trained alongside the target model to keep designated content decodable are contributed.

GPT-3 在多个 NLP 数据集上取得了强劲表现，包括翻译、问答和完形填空任务，以及若干需要即时推理或领域适应的任务，例如乱序词重组、在句子中使用新词、或执行三位数算术运算。GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

本文设计了一种直接处理点云的新型神经网络，较好地尊重了输入点的置换不变性，并为从物体分类、部件分割到场景语义解析等应用提供了统一架构。This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.