Multi-Agent Automated Academic Literature Analysis System: An Intelligent Research Assistant for Hardware and Sensor Fields

This article presents an innovative multi-agent workflow system tailored for the hardware and sensor fields, aiming to address the information overload issue in traditional literature analysis. Through multi-agent collaboration, it achieves end-to-end automation from literature retrieval to in-depth analysis, enhancing academic research efficiency. The system integrates LLM capabilities with a multi-agent architecture, featuring core characteristics such as domain-adaptive understanding and multi-modal data fusion, and has practical application value in scenarios like technical research and device selection.

Academic literature is the cornerstone of knowledge inheritance and innovation, but the millions of new papers published each year pose an information overload challenge for researchers. Traditional literature reviews are time-consuming and prone to missing important findings; in recent years, the rise of large language models and multi-agent systems has provided a new path for automated literature analysis, allowing modularization of retrieval, reading, and organization processes to achieve a qualitative leap in efficiency.

The multi-agent architecture, with advantages of modularity, scalability, and reliability, is suitable for complex academic tasks (drawing on the collaboration mode of human research teams). The core components of the system include:

• Scheduling Agent: Formulates plans and coordinates processes
• Retrieval Agent: Precisely retrieves and filters high-quality literature
• Parsing Agent: Converts PDFs into structured text
• Extraction Agent: Extracts domain key data (device parameters, performance indicators, etc.)
• Analysis Agent: Conducts in-depth analysis of trends and technical contexts
• Verification Agent: Performs cross-validation to ensure result reliability

Tailored to the professionalism of the hardware and sensor fields, the system has the following capabilities:

1. Domain-adaptive understanding: Builds professional terminology libraries, performs structured information extraction, and parses tables and charts
2. Multi-modal data fusion: Text agents process main text, visual agents analyze images and curves, and data agents handle numerical values
3. Iterative in-depth analysis: Hypothesis-driven verification, comparison of different research methods, and analysis of citation networks

The practical applications of the system in the field include:

• Accelerating technical research: Completes traditional weeks-long research in hours and generates structured reports
• Assisting device selection: Compares parameters (sensitivity, power consumption, etc.) and identifies technical risks
• Supporting patent analysis: Extracts technical solutions and innovation points, and understands competitor layouts
• Promoting interdisciplinary integration: Discovers cross-domain research opportunities

Challenges faced by the system and their solutions:

1. Uneven literature quality: Prioritize top journals/conferences, check peer review status, and analyze citation impact
2. Data extraction accuracy: Combine rules and models, multi-agent verification, and confidence score labeling
3. Large-scale literature processing: Distributed parallel architecture, incremental analysis mechanism, and intelligent summary generation

Future directions: Integrate with experimental data systems to form a closed loop, develop personalized research assistants, and build an open scientific ecosystem. Development recommendations: Expand capabilities starting from segmented scenarios, and pay attention to multi-agent frameworks like LangChain/AutoGen and advances in academic methodologies.