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本文回顾现有方法，并融合多种技术从数据与模型规模两方面扩展预训练，提出一条自动化流水线以构建专用、多样且经过筛选的图像数据集，替代自监督文献中常用的未筛选数据。This work revisits existing approaches and combines different techniques to scale the pretraining in terms of data and model size, and proposes an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature.
提出一个模型，基于图像区域上的 CNN、句子上的双向 RNN 以及通过多模态嵌入对齐两种模态的结构化目标，生成图像及其区域的自然语言描述。A model that generates natural language descriptions of images and their regions based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding is presented.
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.
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.
所提出的通用度量空间搜索索引显著优于此前开源的 SOTA 纯向量方法，且该算法与 skip list 结构的相似性便于直接实现均衡的分布式部署。The proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches and similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.
在大规模复杂的移动边缘网络中，涉及具有不同约束的异构设备，这为大规模 FL 实施带来了通信成本、资源分配以及隐私安全方面的挑战。In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.
本综述对图嵌入文献进行全面回顾，并提出两种图嵌入分类法，分别对应不同图嵌入问题设置中的挑战以及现有工作如何在解决方案中应对这些挑战。This survey conducts a comprehensive review of the literature in graph embedding and proposes two taxonomies ofGraph embedding which correspond to what challenges exist in differentgraph embedding problem settings and how the existing work addresses these challenges in their solutions.
在视觉问答与视觉定位任务上对多模态紧凑双线性池化（MCB）进行了广泛评测，结果一致表明 MCB 优于去掉 MCB 的消融版本This work extensively evaluates Multimodal Compact Bilinear pooling (MCB) on the visual question answering and grounding tasks and consistently shows the benefit of MCB over ablations without MCB.
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.
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.
本文在使用 SOTA CNN 的情况下，实现了 16–24 倍的网络压缩，仅带来 1% 的分类准确率损失，并发现针对存储开销最大的全连接层进行压缩时，向量量化方法相比现有矩阵分解方法具有明显优势。This paper is able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN, and finds in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods.
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.
AI 编程助手 Skill（兼容 Claude Code、Codex、OpenCode、Cursor、Gemini CLI 等），可将任意代码、SQL schema、R 脚本、shell 脚本、文档、论文、图片或视频文件夹转换为可查询的知识图谱，应用代码、数据库 schema 与基础设施统一于一张图谱中。AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph.
为每个 Agent 提供跨会话持久上下文——捕获会话中 Agent 的所有行为，经 AI 压缩后注入到未来会话中。支持 Claude Code、OpenClaw、Codex、Gemini、Hermes、Copilot、OpenCode 等。Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
开源 LLM 知识平台：将原始文档转化为可查询的 RAG、自主推理 agent 和可自维护的 Wiki。Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
基于 Hermes Agent 的自主攻击性安全、漏洞悬赏与红队 Agent 框架，具备专用推理技能与多模型 LLM 编排能力。Autonomous Offensive Security, Bug Bounty & Red Teaming Agent Framework powered by Hermes Agent, specialized reasoning skills, and multi-model LLM orchestration.
将任何代码库及其文档、SQL schema、配置文件和 PDF 转化为可查询的知识图谱。适用于 Claude Code、Cursor、Codex 和 Gemini CLI 的 /graphify skill：本地确定性 AST 解析，每条边都有解释，无需向量存储。Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
Adobe Illustrator 的开源 clean-room 纯 Rust 重新实现。An open-source, clean-room reimplementation of Adobe Illustrator, built in pure Rust.