arXiv cs.AI· Xin Xu, Ke Cheng, Hao Wang, Yangru Huang, Yanghao Xiao, Zhichao Chen, Zhisheng Zhong, Xun Liu, Yi Li, Qian Li, Haibin Lv, Jun Zhang, Jie Jiang, Zhouchen Lin·· 3 天前AI 评分27
SpecRSI：面向智能体系统的推测式递归自我改进
Drafting Agents, Not Just Tokens: Speculative Recursive Self-Improvement for Agentic Systems
AI 导读
研究者提出 Speculative Recursive Self-Improvement（SpecRSI），在智能体前代版本尚未完全评估前就起草可执行的后续智能体，但采用前必须完成验证。
正文 · 原文
Authors:Xin Xu, Ke Cheng, Hao Wang, Yangru Huang, Yanghao Xiao, Zhichao Chen, Zhisheng Zhong, Xun Liu, Yi Li, Qian Li, Haibin Lv, Jun Zhang, Jie Jiang, Zhouchen Lin
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.
| Comments: | 47 pages; code available at this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.16491 [cs.LG] |
| (or arXiv:2610.16491v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.16491 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xin Xu [view email]
[v1]
Mon, 12 Oct 2026 09:43:48 UTC (1,743 KB)
来源：arXiv cs.AI · arxiv.org