Researchers build self-improving AI agent that invents its own operators for exact answers
A team of researchers has developed a self-improving AI agent that can grow its own domain-specific language (DSL) without external rewards, judges, or answer keys. The agent uses two nested loops: an inner loop answers new questions only when two independently generated expressions agree on every record, while an outer loop discovers missing capabilities and invents new operators. These…
Key points
- Agent invents new operators for exact answers without rewards, judges, or ground truth
- Nested loops verify correctness via independent expression agreement and conservation laws
- Method expands answerable questions by over tenfold in unrelated domains like business and clinical data
The method successfully triples the set of answerable questions in a small domain and extends its capabilities more than tenfold in unrelated domains like business-ledger and clinical-encounter question answering. The agent’s admission rule rejects 138 out of 138 buggy operator variants, demonstrating empirical soundness. The mechanism also transfers across different model families, with performance varying based on each proposer’s DSL fluency.
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