Research map / Large language models / Retrieval and knowledge
SciSpace Copilot: Empowering Researchers through Intelligent Reading Assistance
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Presents SciSpace Copilot, a commercial RAG-based AI reading assistant for research papers offering cited question answering, explanations, summaries, notes, highlights, related-paper search and 75+ language support.
Problem
Researchers struggle to keep up with the volume of publications and to understand complex paper content (text, equations, tables) quickly, and existing tools do not address these needs well.
Approach
A deployed document-grounded assistant that combines parsing, embedding-based vector search with structure-aware context extraction, and LLM prompting (OpenAI, Anthropic, open-source models) to answer questions with exact citations and provide reading-support features in a single tool.
Key results
- No quantitative results are reported; the paper describes features and deployment usage (thousands of daily users, 75+ languages).
Limitations
- No quantitative evaluation of answer accuracy or citation quality is reported.
- Relies on proprietary LLM APIs whose identity per feature is not specified.
Experimental setup
Datasets: 280 million paper corpus (related-paper search)
Builds on: Retrieval-Augmented Generation
Closest papers by meaning
- SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers (NeurIPS 2024 · similarity 0.84)
- Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning (ICLR 2026 · similarity 0.83)
- Docopilot: Improving Multimodal Models for Document-Level Understanding (CVPR 2025 · similarity 0.83)
- SciResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery (NeurIPS 2026 · similarity 0.83)
- Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting (ICLR 2025 · similarity 0.83)
- GEAR-Up: Generative AI and External Knowledge-Based Retrieval: Upgrading Scholarly Article Searches for Systematic Reviews (AAAI 2024 · similarity 0.82)
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