Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification
Recursive self-improvement (RSI) enables agents to iteratively optimize their workflows via execution feedback. However, standard RSI typically operates as a first-order optimizer: it repeatedly patches surface-level parameters in response to immediate failure symptoms, often leading to trial-and-error thrashing without resolving underlying mechanisms. To address this limitation, we introduce SO-RSI, a framework that elevates workflow optimization to a second-order diagnostic inquiry, investigating why failures occur before committing to structural interventions. SO-RSI passively monitors execution traces for three structural anomalies (recurrence, opposing edits, and expectation mismatch...
| Authors | Yuxuan Jiang, Aditya Vempaty, Ashish Jagmohan |
|---|---|
| Primary category | cs.AI |
| Published | 5 October 2026 |
| Last updated at source | 6 October 2026 |
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