Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems

Abstract:Improving an agent through successive revisions requires balancing exploration and evaluation under a limited budget. A narrow search can miss useful sequences of edits, while fully evaluating every intermediate version can exhaust the budget before those sequences yield a verified improvement. We introduce Speculative Recursive Self-Improvement (SpecRSI), which drafts executable successor agents before their predecessors are fully evaluated, while requiring complete verification before adoption. Verification-Aware Tree Search (VATS) allocates the budget between further edits and outstanding checks using the predicted verified return of a continuation. Group Verification Optimization (GVO) learns the improver from completed searches while keeping the real evaluator fixed. In the main scientific comparison, terminal gains exceed Janus by 33.3%--40.0%. Additional evaluations show that SpecRSI outperforms Janus in all 50 scientific backbone--task pairs and exceeds MetaRSI-v1 by 1.85%--2.38% in macro-average system score across twenty backbones. Coding-validation success reaches 69.4% versus HGM's 64.3% at a 20.8% lower target-cost index.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.16491 [cs.LG]

(or arXiv:2610.16491v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.16491 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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