论文

本文对一个自 2026 年 3 月起持续运行的个人助理 Agent 运行时中的静默失败进行纵向研究，该系统包含约 40 个定时任务、8 个 LLM 提供商、一个工具治理代理以及一个知识库记忆层，由 4,286 个单元测试和 827 项治理检查守护。A longitudinal study of silent failures in a personal-assistant agent runtime in continuous production since March 2026, with roughly 40 scheduled jobs, 8 LLM providers, a tool-governance proxy, and a knowledge-base memory plane, defended by 4,286 unit tests and 827 governance checks is presented.

结果表明，纯视觉设置会降低准确率并增加 token 成本，因为 Agent 缺乏足够的符号化细节，需通过重复的视觉查询进行补偿；研究指向一种面向下一代编码 Agent 的实用文本与视觉混合设计。The results show that a strictly vision-only setup degrades accuracy and increases token cost, because agents lack sufficient symbolic detail and compensate with repeated visual queries, and point to a practical hybrid text-and-vision design for next-generation coding agents.

本文研究了在采用极小卷积滤波器的架构下，卷积网络深度对大规模图像识别精度的影响，并表明将深度推进至 16-19 个权重层，可在先前 SOTA 配置基础上取得显著提升。This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

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.

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.

本文开发并发布了 Llama 2，这是一系列参数规模从 70 亿到 700 亿不等的预训练与微调大语言模型（LLMs），有望成为闭源模型的合适替代品。This work develops and releases Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters, which may be a suitable substitute for closed-source models.

本研究证实了激活函数之间存在统计显著的性能差异，从而再次确认了非饱和函数在深度架构中的必要性，并恢复了对此前文献的恰当历史归属。This study confirms a statistically significant performance variance among activations, thus reaffirming the necessity of non-saturating functions in deep architectures, and restores proper historical attribution to prior literature.

一种基于二部交互图上可微分消息传递的图自编码器框架，在标准协同过滤基准上表现出竞争力，并优于近期的 SOTA 方法。A graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph that shows competitive performance on standard collaborative filtering benchmarks and outperforms recent state-of-the-art methods.

本文介绍 MLlib，Spark 的开源分布式机器学习库，可为多种学习场景提供高效功能，并包含若干底层的统计、优化和线性代数原语。MLlib is presented, Spark's open-source distributed machine learning library that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.

本文提出了一个自然场景文本识别框架，无需任何人工标注数据，并以整体方式对整幅图像进行单词识别，区别于过去基于字符的识别系统。This work presents a framework for the recognition of natural scene text that does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past.

本文提出了一种新的解码策略——self-consistency，用于替代思维链 prompt 中使用的朴素贪心解码：首先采样一组多样化的推理路径，而非仅取贪心路径，然后通过对采样路径进行边缘化来选择最一致的答案。This paper proposes a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting that first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths.

文章提出了 SegFormer，一个简单高效且强大的语义分割框架，将 Transformers 与轻量级 MLP 解码器统一，并在 Cityscapes-C 上展示了出色的零样本鲁棒性。SegFormer is presented, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders and shows excellent zero-shot robustness on Cityscapes-C.

结果表明，使用人类反馈进行微调是使语言模型与人类意图对齐的一个有前景的方向，在真实性方面有所提升，并减少了有毒输出的生成，同时在公开 NLP 数据集上的性能回归极小。The results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent and showing improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.

在三种大语言模型上的实验表明，思维链提示能够在一系列算术、常识和符号推理任务上提升性能。Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks.

本工作提出两种参数削减技术以降低 BERT 的内存占用并提升训练速度，并采用一种聚焦于建模句子间连贯性的自监督损失。This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

本工作提出 BERTScore——一种文本生成自动评估指标，与人类判断的相关性更强，并在模型选择性能上优于现有指标。This work proposes BERTScore, an automatic evaluation metric for text generation that correlates better with human judgments and provides stronger model selection performance than existing metrics.

本文详细阐述了驱动 PyTorch 实现的原则及其在架构中的体现，并解释了 runtime 关键组件的精心且务实的实现如何使其协同工作以获得出色的性能。This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.

这项系统性研究在数十项语言理解任务上比较了预训练目标、架构、无标注数据集、迁移方法及其他因素，并在涵盖摘要、问答、文本分类等的许多基准上取得了 SOTA 结果。This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

本文提出了一种基于迭代模型平均的深度网络联邦学习实践方法，并进行了广泛的实证评估，考虑了五种不同的模型架构和四个数据集。This work presents a practical method for the federated learning of deep networks based on iterative model averaging, and conducts an extensive empirical evaluation, considering five different model architectures and four datasets.

本文提出了 mixup，一种通过对样本对及其标签的凸组合来训练神经网络的简单学习原则，提升了 SOTA 神经网络架构的泛化能力。This work proposes mixup, a simple learning principle that trains a neural network on convex combinations of pairs of examples and their labels, which improves the generalization of state-of-the-art neural network architectures.

本文提出了一种通用的无监督学习方法——对比预测编码（Contrastive Predictive Coding），用于从高维数据中提取有用的表征，并在语音、图像、文本和 3D 环境中的强化学习四个不同领域取得了出色的性能。This work proposes a universal unsupervised learning approach to extract useful representations from high-dimensional data, which it calls Contrastive Predictive Coding, and demonstrates that the approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

本文提出了一种双时间尺度更新规则（TTUR），用于在任意 GAN 损失函数下使用 SGD 训练 GAN，并引入了 Frechet Inception Distance（FID），相比 Inception Score 能更好地捕捉生成图像与真实图像之间的相似性。This work proposes a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions and introduces the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score.

本文提出了一种通过创建在所有边缘设备之间全局共享的小型数据子集来改进非独立同分布数据训练的策略，并表明在 CIFAR-10 数据集上，仅共享 5% 的全局数据即可将准确率提升 30%。This work presents a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices, and shows that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.