Insights on AI research for regulated domains
The era of making models bigger is ending. Five paradigms are replacing brute-force scaling — hybrid architectures, inference-time reasoning, world models, self-improvement loops, and hardware co-design. What each means for AI in regulated industries.
First-stage retrieval finds 100 matches. Reranking identifies the 5 that actually answer your question. Here's why that distinction matters for legal AI.
Keyword search finds exact article numbers. Semantic search finds related concepts. Hybrid search does both — and the difference is measurable.
A search engine finds text. A knowledge graph navigates relationships: which article amends which, which ruling interprets what, which exception overrides the rule. Belgian tax law is a web of cross-references — and a knowledge graph is the map.
Before your legal AI tool can answer a question, it has to cut the law into pieces. The way it cuts determines whether the answer includes the exception that changes everything — or misses it entirely.
A ministerial circular and a Court of Cassation ruling look the same to most AI search systems. That's not a minor flaw — it's a fundamental gap that makes every answer unreliable.
How retrieval-augmented generation works, why basic RAG still hallucinates, and what a search-RAG fusion architecture adds for tax professionals.
Fine-tuning memorizes yesterday's law. RAG looks up today's. For Belgian tax professionals, this architecture choice determines whether your AI tool is current or confidently outdated.
Why language models invent legal citations, what makes Belgian tax especially vulnerable, and three defenses that actually work.