Banger paper on self-evolving ontologies for agents.

You just can't go wrong with implementing an ontology layer for your agents.

This paper shows exactly why.

The show that GPT-5.5 gains 26.7 points on DDR-Bench when the data agent can query an ontology of the data it works with.

Why is this useful?

Data agents normally see tables, files and databases through generic tools, reading column names and file paths one call at a time.

The alternative is a hand-written semantic layer pasted into the prompt, which does not scale to many sources.

EvoOntology builds the ontology with a dedicated agent and serves it as an MCP server with schema, content and tool layers.

The data agent queries it at runtime.

The ontology is then edited in small typed steps, and each edit is kept only if a paired evaluation on the same backbone shows it helps.

Across six backbones on DDR-Bench, accuracy rises 17.8 points on average, from 4.8 on Qwen3.5-Flash to 26.7 on GPT-5.5. On BIRD, execution accuracy rises 7.4 points. Edits to the tool layer account for 57% of the gain from evolution.

Paper: https://arxiv.org/abs/2609.15779

Chat with Paper: https://academy.dair.ai/papers/evoontology-a-self-evolving-ontology-layer-for-data-agents-2609.15779