AI

A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering

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Briefings

A self-improving agent learned to generate exact answers without external rewards or ground truth by using a frozen model to evaluate operator validity through independent expression agreement and conservation laws. The agent self-extended its answerable question set across two domains, growing its capabilities by more than tenfold and nearly twentyfold, while maintaining perfect accuracy on all generated questions.

Key points

• The agent uses a frozen model to evaluate new operators by checking if two independently written expressions agree on every record and if the result obeys a conservation law.
• The agent self-extended its answerable question set across two domains, growing its capabilities by more than tenfold and nearly twentyfold, while maintaining perfect accuracy on all generated questions.

Context

This research addresses the challenge of self-improving agents learning to compute with no reward, no judge, and no answer key. The method relies on a frozen model to evaluate operator validity through independent expression agreement and conservation laws, proving that agents can grow their own capabilities without external validation.

Limitations

The study is limited to a small, exact domain-specific language with two independent starting sets. The agent's ability to self-extend on unrelated schemas was tested with business-ledger and clinical-encounter question answering, but the generalizability to other domains remains unverified.

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