Research Copilot: An Autonomous AI Research Engineering Platform for Automated Literature Discovery, Evidence Grounding, and Novelty Analysis

Domain: Artificial Intelligence in Scientific Research Engineering, Autonomous Literature Synthesis, and Evidence-Grounded Knowledge Graph Systems.Aim: This paper aims to eliminate scientific hallucination, break literature search silos, and automate the identification of underexplored research gaps through an end-to-end autonomous research engineering platform. Methodology: The proposed system integrates an 8-source federated search aggregator with multi-tier DOI/arXiv deduplication, a multi-attribute 6-Factor Scientific PaperRank algorithm combining lexical BM25 relevance with dynamic citation graph prestige, a deterministic coordinate-indexed PyMuPDF extraction engine for pixel-level bounding-box evidence verification, an attributed NetworkX knowledge graph with a combinatorial Method-Dataset gap realization matrix, and progressive dual-layer WebAssembly document rendering. Result: Empirical evaluations demonstrate that Research Copilot completes federated multi-source ingestion in under 1.8 seconds, achieves 100% deterministic bounding-box verification accuracy with a 0% hallucination rate on verified scientific claims, reduces literature review and cross-paper gap analysis turnaround by over 70%, and operates at an ultra-low inference footprint of under 250 MB RAM with Gemini 2.0 Flash Lite synthesis costs under $0.001 per hypothesis.