PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing
Junyi Hou (National University of Singapore), Andre Lin Huikai (National University of Singapore), Nuo Chen (National University of Singapore), Yiwei Gong (Independent Researcher, Singapore), Bingsheng He (National University of Singapore)

TL;DR
PaperDebugger introduces an in-editor, multi-agent system for academic writing that enables deep, context-aware interactions within LaTeX editors like Overleaf, integrating reasoning, revision, and literature tools seamlessly.
Contribution
It presents a novel plugin-based multi-agent architecture for in-editor academic writing, addressing synchronization, security, and extensibility challenges.
Findings
Successful integration of LLM reasoning within LaTeX editors
Active user engagement demonstrated through analytics
Validated practicality of in-editor, agentic writing assistant
Abstract
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…
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Taxonomy
TopicsScientific Computing and Data Management · Model-Driven Software Engineering Techniques · Topic Modeling
