Large language models are increasingly embedded into academic writing workflows, yet existing assistants remain external to the editor, preventing deep interaction with document state, structure, and revision history. This separation makes it impossible to support agentic, context-aware operations directly within LaTeX editors such as Overleaf. We present PaperDebugger, an in-editor, multi-agent, and plugin-based academic writing assistant that brings LLM-driven reasoning directly into the writing environment. Enabling such in-editor interaction is technically non-trivial: it requires reliable bidirectional synchronization with the editor, fine-grained version control and patching, secure state management, multi-agent scheduling, and extensible communication with external tools. PaperDebugger addresses these challenges through a Chrome-approved extension, a Kubernetes-native orchestration layer, and a Model Context Protocol (MCP) toolchain that integrates literature search, reference lookup, document scoring, and revision pipelines. Our demo showcases a fully integrated workflow, including localized edits, structured reviews, parallel agent execution, and diff-based updates, encapsulated within a minimal-intrusion user interface (UI). Early aggregated analytics demonstrate active user engagement and validate the practicality of an editor-native, agentic writing assistant. More details about this demo and video could be found at https://github.com/PaperDebugger/PaperDebugger.

翻译：大型语言模型正日益融入学术写作工作流，然而现有辅助工具仍处于编辑器外部，无法与文档状态、结构及修订历史进行深度交互。这种分离导致无法在诸如Overleaf等LaTeX编辑器中直接支持具备自主性与上下文感知的操作。本文提出PaperDebugger，一种内置于编辑器的、基于插件的多智能体学术写作助手，它将LLM驱动的推理直接引入写作环境。实现此类编辑器内交互在技术上具有挑战性：需要与编辑器进行可靠的双向同步、细粒度的版本控制与补丁管理、安全的状态维护、多智能体调度，以及与外部工具的可扩展通信。PaperDebugger通过一个经Chrome认证的扩展程序、一个Kubernetes原生的编排层，以及一个集成文献检索、参考文献查找、文档评分和修订流程的模型上下文协议（MCP）工具链，应对了这些挑战。我们的演示展示了一个完全集成的工作流，包括局部化编辑、结构化审阅、并行智能体执行和基于差异的更新，所有这些都封装在一个低侵入性的用户界面（UI）中。早期汇总分析显示了积极的用户参与度，并验证了这种编辑器原生、具备自主性的写作助手的实用性。更多演示详情及视频可见https://github.com/PaperDebugger/PaperDebugger。