arXiv cs.AI· Yutong Cheng, Haifeng Chen, Peng Gao, Wei Cheng·· 3 天前AI 评分44
SpecForge：通过行为规范实现编码智能体的递归自我改进
SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications
AI 导读
SpecForge 是一个多前沿树搜索框架，让冻结权重的编码智能体通过迭代优化行为规范实现递归自我改进，在 DevEval+ 上以 Claude-4.5-Sonnet 达到 94.9% 解决率，较最强基线平均提升 5.7 个百分点。
正文 · 原文
Abstract:Coding agents based on frozen language models can improve their performance by refining the external context they rely on, without updating model weights. We study recursive self-improvement through behavioral specifications: natural-language code documentation that describes the behavior of program entities and guides agents in generating correct code. We formulate a self-improvement loop in which an agent generates code from its current specifications, evaluates it with execution-verified tests, and refines the specifications based on observed failures. The improved specifications then guide subsequent generation, progressively improving the agent's understanding of the codebase. However, recursive refinement is challenging because program entities are interdependent. Improving the specification of one entity can inadvertently degrade the correctness of others, a phenomenon we call output coupling. This creates a whack-a-mole effect in which local improvements cause regressions elsewhere, preventing consistent progress. We introduce SpecForge, a multi-frontier tree search framework that addresses this challenge with three mechanisms: (i) Pareto-guided state selection preserves complementary specification states and prevents premature commitment; (ii) dependency-constrained entity selection prioritizes callee-before-caller refinement; and (iii) error-diversified expansion explores multiple refinements using different subsets of test failures. On DevEval+, SpecForge achieves the best performance across five backbone models, including a 94.9% solve rate with Claude-4.5-Sonnet and a 5.7-percentage-point average improvement over the strongest baseline. The improved specifications also make the agent better at tasks outside the loop: they transfer to cross-language translation and to new-feature implementation, where they outperform the benchmark-provided requirement documents.
| Comments: | Accepted to NeurIPS 2026 (Main Track) |
| Subjects: | Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.15708 [cs.AI] |
| (or arXiv:2610.15708v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15708 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yutong Cheng [view email]
[v1]
Sun, 11 Oct 2026 15:46:22 UTC (1,478 KB)
来源：arXiv cs.AI · arxiv.org