The TIB AIssistant is a scholarly AI platform designed to support the full research lifecycle through human-machine collaboration. It integrates large language models (LLMs), knowledge graphs, and external scholarly tools within a modular orchestration framework governed by a Model-Context Protocol.
The system combines Retrieval-Augmented Generation (RAG), a shared data store, and a configurable tool library to assist researchers with literature review, data analysis, manuscript preparation, and post-publication activities. Rather than automating research outcomes, the platform emphasizes transparency, provenance tracking, and human-in-the-loop validation, positioning AI as an augmentative partner rather than a replacement for scientific judgment.
Benefits & outcomes
- End-to-end AI support across the research lifecycle
- Reduced hallucination through RAG and external knowledge retrieval
- Transparent provenance and reproducibility of AI-generated outputs
- Flexible integration of external scholarly tools and data sources
- Strong emphasis on human agency, oversight, and error correction
- Supports customization across disciplines and research workflows
Notes & quotes
https://arxiv.org/pdf/2512.16442
The architecture treats research systems as inherently dynamic and subject to entropy, prioritising adaptability, modularity, and graceful degradation over rigid automation. AI outputs are explicitly framed as suggestions requiring expert scrutiny rather than authoritative results.
Publisher / company: TIB – Leibniz Information Centre for Science and Technology
Date of case study: 08/01/2026
Source: https://bbg-news.com/smarter-research-how-ai-assistants-are-changing-the-game/