ResearchPilot: A Local-First Multi-Agent System for Literature Synthesis and Related Work Drafting
- URL: http://arxiv.org/abs/2603.14629v1
- Date: Sun, 15 Mar 2026 22:02:59 GMT
- Title: ResearchPilot: A Local-First Multi-Agent System for Literature Synthesis and Related Work Drafting
- Abstract summary: ResearchPilot is an open-source, self-hostable multi-agent system for literature-review assistance.<n>Given a natural-language research question, it retrieves papers from Semantic Scholar and arXiv.<n>It extracts structured findings from paper abstracts, synthesizes cross-paper patterns, and drafts a citation-aware-work section.
- Score: 3.272585676511349
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: ResearchPilot is an open-source, self-hostable multi-agent system for literature-review assistance. Given a natural-language research question, it retrieves papers from Semantic Scholar and arXiv, extracts structured findings from paper abstracts, synthesizes cross-paper patterns, and drafts a citation-aware related-work section. The system combines FastAPI, Next.js, DSPy, SQLite, and Qdrant in a local-first architecture that supports bring-your-own-key model access and remote-or-local embeddings. This paper describes the system design, typed agent interfaces, persistence and history-search mechanisms, and the engineering tradeoffs involved in building a transparent research assistant. Rather than claiming algorithmic novelty, we present ResearchPilot as a systems contribution and evaluate it through automated tests and end-to-end local runs. We discuss limitations including external API rate limits, abstract-only extraction, incomplete corpus coverage, and the lack of citation verification.
Related papers
- SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery [48.99744520105086]
SciForge is a multimodal research-native AI workbench.<n>It reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular agent-accessible services.
arXiv Detail & Related papers (2026-07-17T15:13:03Z) - Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction [127.64173950476702]
We study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools.<n>This approach requires no offline indexing and adapts naturally to evolving local corpora.<n>Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines.
arXiv Detail & Related papers (2026-05-03T19:13:11Z) - Lishu: A Real-Source Research Workbench for Elite Business Journal Search, Analysis, and Writing Support [13.418736804875303]
Lishu is a deployable web artifact for searching, monitoring, and interpreting literature from elite business and management journals.<n>The system integrates the UTD-24 and Financial Times 50 (FT50) journal pools and combines Crossref, OpenAlex, Unpaywall, and optional CORE enrichment.<n>Unlike static journal directories or general-purpose academic search engines, the artifact is explicitly scoped to high-status management outlets.
arXiv Detail & Related papers (2026-04-09T07:52:07Z) - Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework [82.66443886385125]
Paper Circle is a discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature.<n>The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-agent scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes such as concepts, methods, and figures.
arXiv Detail & Related papers (2026-04-07T17:59:58Z) - FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents [53.03492387564392]
We introduce FS-Researcher, a file-system-based framework that scales deep research beyond the context window via a persistent workspace.<n>A Context Builder agent browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length.<n>A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts.
arXiv Detail & Related papers (2026-02-02T03:00:19Z) - Intelligent Scientific Literature Explorer using Machine Learning (ISLE) [0.797970449705065]
This paper presents an integrated system for scientific literature exploration that combines large-scale data acquisition, hybrid retrieval, semantic topic modeling, and heterogeneous knowledge graph construction.<n>The proposed framework contributes a foundation for AI-assisted scientific discovery.
arXiv Detail & Related papers (2025-12-14T16:54:24Z) - WisPaper: Your AI Scholar Search Engine [55.07907253175705]
textscWisPaper is an intelligent academic retrieval and literature management platform.<n>It provides a closed-loop workflow that seamlessly connects literature discovery, management, and continuous tracking of research frontiers.<n>The platform is publicly accessible and serves researchers across academia and industry.
arXiv Detail & Related papers (2025-12-07T15:10:20Z) - AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance [0.0]
AISAC builds on technologies - LangGraph for orchestration, FAISS for vector search, and persistence.<n>System implements prompt-engineered agents coordinated via LangGraph's StatePlanner.<n>A project-driven bootstrap allows research teams to customize tools, prompts, and data sources without modifying core code.
arXiv Detail & Related papers (2025-11-18T01:51:05Z) - Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation [41.659285482346235]
We present texttfreephdlabor, an open-source multiagent framework featuring textitfully dynamic determined by real-time agent reasoning.<n>The framework provides comprehensive infrastructure including textitautomatic context compaction, textitworkspace-based communication to prevent information degradation, textitmemory persistence across sessions, and textitnon-blocking human intervention mechanisms.
arXiv Detail & Related papers (2025-10-17T13:13:32Z) - HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
arXiv Detail & Related papers (2025-08-01T20:30:42Z) - Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - System for systematic literature review using multiple AI agents: Concept and an empirical evaluation [3.453564255183234]
Systematic literature review ( SLR) is foundational to evidence-based research.<n>We introduce a novel multi-AI-agent system designed to fully automate SLRs.<n>We show how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
arXiv Detail & Related papers (2024-03-13T10:27:52Z) - Retrieving and Reading: A Comprehensive Survey on Open-domain Question
Answering [62.88322725956294]
We review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques.
We introduce modern OpenQA architecture named Retriever-Reader'' and analyze the various systems that follow this architecture.
We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used.
arXiv Detail & Related papers (2021-01-04T04:47:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.