Emir BardakçıXiaomi recently launched its revolutionary Mi-BRAG knowledge base question and answer system, a major technological leap in artificial intelligence. This new system revolutionally redefines the process of processing knowledge through four advanced technology systems already in use in various Xiaomi ecosystem applications such as Xiao AI personal assistants, in-car question-answering systems, and support product services.
Mi-BRAG is a significant advancement in the management and leveraging of their knowledge bases by firms to overcome major weaknesses that have undermined even the most superior LLMs. Though LLMs provide tremendous generation strength, there are major weaknesses in the form of high knowledge update expenses, lack of insight into proprietary corporate knowledge bases, and the risk of data breaches. Xiaomi’s use of Retrieval-Augmented Generation or RAG technology comes as a best practice approach to overcome these weaknesses in addition to incorporating several value-added developments.
Key Features of Mi-BRAG
Full-Format Compatibility
Mi-BRAG features a clever parsing mechanism that has the ability to loselessly parse over a decade’s worth of document formats such as PDF, Word, and Excel. Such versatility forms a stable base for corporations to establish a comprehensive knowledge base without the limitations of a certain format.
Full-Modal Analysis
The system cuts through the usual textual constraints, properly parsing elaborate information contained in images, tables, blended text and images, and other composite forms that earlier systems have not been in a position to process efficiently.
Multilingual Q&A
Mi-BRAG enables document parsing and interactive query-answering in many major languages, essentially breaking language barriers in the circulation of knowledge in global operations.
Fine-Grained Traceability
Mi-BRAG identifies the original document and reference point for each result using dynamic traceability positioning technology so that users can trace the source of information in a single click to establish a secure knowledge environment through complete accountability.
Technical Innovations
The Big Model group in Xiaomi designed Mi-BRAG on four core elements:
- B. Knowledge Base Construction: Integration of cross-modal knowledge and building a “pyramid-shaped” dynamic knowledge system
- Knowledge Retrieval (R): Better query understanding ability
- Enhancement of Knowledge (A): Multi-dimensional semantic
- Response Generation (G): Knowledge injection to scenario-specific models for better rejection of irrelevant information
As defined by Xiaomi, the knowledge base generation module plays a specialized role that has a considerable influence on the question-answering performance, hence why it was explicitly added to the framework name.
Industry-Leading Performance
Mi-BRAG has already achieved outstanding performance, ranking atop the SuperCLUE-RAG generation ability benchmark in April 2025. The model performed outstandingly in the three standard settings: single-document question-answering, multi-document question-answering, and search question-answering.
When tested on the demanding ASQA long-form factual test set, Mi-BRAG surpassed market-leader large models in multi-document generation precision and recall as well as traceability.
Current Applications and Future Development
Mi-BRAG has already found applications in a few Xiaomi ecosystem apps:
- Xiao AI personal information Q&A
- Smart car Q&A assistant systems
- Product support services
In the future, Xiaomi’s major model group will keep pushing forward several major technological areas such as end-side computing power optimization, cloud-edge collaborative architecture, compression of memory, and power management. The group also aims to enhance multimodal technology integration to build a complete intelligent Q&A system for text, voice, and vision.
These advancements will add new application scenarios in the areas of both automotive and home settings, reinforcing the strategic concept of Xiaomi’s “ecosystem of people, cars, and homes.”
Source: IT Home
