Agentic Test-Time Scaling (TTS) has delivered state-of-the-art (SOTA) performance on complex software engineering tasks such as code generation and bug fixing. However, its practical adoption remains limited due to significant computational overhead, primarily driven by two key challenges: (1) the high cost associated with deploying excessively large ensembles, and (2) the lack of a reliable mechanism for selecting the optimal candidate solution, ultimately constraining the performance gains that can be realized. To address these challenges, we propose Entropy-Guided Stepwise Scaling (EGSS), a novel TTS framework that dynamically balances efficiency and effectiveness through entropy-guided adaptive search and robust test-suite augmentation. Extensive experiments on SWE-Bench-Verified demonstrate that EGSS consistently boosts performance by 5-10% across all evaluated models. Specifically, it increases the resolved ratio of Kimi-K2-Intruct from 63.2% to 72.2%, and GLM-4.6 from 65.8% to 74.6%. Furthermore, when paired with GLM-4.6, EGSS achieves a new state-of-the-art among open-source large language models. In addition to these accuracy improvements, EGSS reduces inference-time token usage by over 28% compared to existing TTS methods, achieving simultaneous gains in both effectiveness and computational efficiency.

翻译：智能体测试时扩展（TTS）在代码生成与缺陷修复等复杂软件工程任务上已展现出最先进的性能。然而，其实际应用仍受限于显著的计算开销，这主要由两个关键挑战导致：(1) 部署超大规模集成模型带来的高昂成本；(2) 缺乏可靠的机制以选择最优候选解决方案，最终制约了可实现的性能提升。为应对这些挑战，我们提出基于熵引导的渐进式扩展（EGSS），这是一种新颖的TTS框架，通过熵引导的自适应搜索与鲁棒的测试套件增强技术，动态平衡效率与效能。在SWE-Bench-Verified数据集上的大量实验表明，EGSS在所有评估模型上持续将性能提升5-10%。具体而言，它将Kimi-K2-Intruct的解决率从63.2%提升至72.2%，将GLM-4.6的解决率从65.8%提升至74.6%。此外，当与GLM-4.6结合使用时，EGSS在开源大语言模型中达到了新的最先进水平。除精度提升外，与现有TTS方法相比，EGSS将推理阶段的令牌使用量降低超过28%，实现了效能与计算效率的同步提升。