Didact: A Cross-Domain Capability Discovery System for Defence

2026-06-05 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

Didact is a prototype cross-domain capability discovery system designed to assist policymakers in defence and defence-aligned sectors. It addresses the challenge of monitoring rapidly evolving research and sector priorities, which are often fragmented across heterogeneous formats and siloed repositories. Didact integrates publicly available Australian defence reports and policy documents with a purpose-built knowledge graph derived from Australian research publications. The system facilitates natural language conversations for policy-oriented workflows, leveraging a composite retrieval-augmented generation (RAG) pipeline. A key feature is its interactive Evidence Rail, which visualizes retrieved evidence and source relationships. Evaluation of Didact's output quality and runtime demonstrates its utility, and while developed for the Australian context, it is adaptable to other domains facing similar knowledge fragmentation.

Key takeaway

For Directors of AI/ML or policymakers in defence-aligned sectors tasked with monitoring complex, fragmented information, you should evaluate the potential of composite RAG pipelines and knowledge graphs. Didact demonstrates how integrating disparate public reports and research publications, coupled with an interactive evidence visualization, can significantly improve capability discovery and auditability. Consider adapting this approach to build more efficient, auditable intelligence systems for your specific domain, especially where knowledge silos impede strategic decision-making.

Key insights

Didact enables cross-domain defence capability discovery by integrating fragmented knowledge sources via RAG and a knowledge graph.

Principles

• Fragmented knowledge hinders policy monitoring.
• Visualizing evidence relationships improves auditability.

Method

Didact employs a composite retrieval-augmented generation (RAG) pipeline to facilitate natural language conversations and an interactive Evidence Rail.

In practice

• Integrate public reports with research publications.
• Visualize evidence and source relationships.
• Adapt system to other fragmented knowledge domains.

Topics

• Didact
• Defence Policy
• Capability Discovery
• Knowledge Graphs
• Retrieval-Augmented Generation
• Natural Language Processing
• Evidence Visualization

Best for: Executive, AI Architect, NLP Engineer, Policy Maker, AI Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.