A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
Quick Answer
This paper shows that A self-improving agent can autonomously grow its own domain-specific language (DSL) for exact question answering without external validation, achieving a threefold increase in answerable questions.
Quick Take
The agent's mechanism effectively rejects buggy operators while extending capabilities across unrelated domains, demonstrating significant scalability and robustness.
Key Points
- The agent operates without rewards, judges, or answer keys.
- Inner loop triples the set of answerable questions.
- Outer loop invents new operators based on independent agreement.
- Mechanism rejects 138 buggy operator variants reliably.
- Self-extension achieved in unrelated domains like business and clinical questions.
DeepSignal Analysis
What happened
A self-improving agent developed a domain-specific language (DSL) for exact question answering without external validation. This approach led to a threefold increase in the number of answerable questions. The agent's mechanism effectively filters out incorrect operators while expanding its capabilities across unrelated domains.
Key evidence
- The agent's inner loop answers new questions only when two independently written expressions agree on every record.
- The outer loop discovers new capabilities and authors operators, admitting them only when a second independent implementation agrees.
- The same mechanism self-extends to two additional domains, increasing their answerable sets by more than tenfold and nearly twentyfold.
Why it matters
This research demonstrates a novel approach to operator induction that does not rely on external validation, which could enhance the scalability and robustness of AI systems. By effectively rejecting incorrect operators, the agent improves its accuracy in answering questions, which is critical in applications requiring precise information retrieval.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly right: over a table of transactions, a total is either right or wrong. We ask a harder question: can an agent grow what it can compute with no reward, no judge, and no answer key, and still be exactly right? A single frozen model runs two nested loops over a small, exact domain-specific language (DSL). The inner loop answers new questions only when two independently written expressions agree on every record. The outer loop discovers its own missing capability and authors an operator for it, admitted only when an independent second implementation agrees and the result obeys a conservation law, all without ground truth. The inner loop roughly triples the answerable-question set, and the outer loop self-extends on disjoint starting sets, inventing operators outside the starting closure; a no-induction control answers none of the new questions. The admission rule is empirically sound: it rejects 138 of 138 buggy operator variants under an independent reference, even one written by a different model family, where the admit-if-it-runs, self-consistency, and compression signals the field relies on admit most of them. Unmodified, the same mechanism self-extends on two further domains with unrelated schemas, business-ledger and clinical-encounter question answering, growing their answerable sets more than tenfold and nearly twentyfold. The loop transfers across model families too: run with further proposers, the gate stays sound and independent families self-extend comparably, with only the induction yield tracking each proposer's DSL fluency.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.20809 [cs.AI] |
| (or arXiv:2610.20809v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20809 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yun-Shiuan Chuang [view email]
[v1]
Wed, 14 Oct 2026 18:55:34 UTC (1,403 KB)
— Originally published at arxiv.org
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