SWE-bench has emerged as the premier benchmark for evaluating Large Language Models on complex software engineering tasks. While these capabilities are fundamentally acquired during the mid-training phase and subsequently elicited during Supervised Fine-Tuning (SFT), there remains a critical deficit in metrics capable of guiding mid-training effectively. Standard metrics such as Perplexity (PPL) are compromised by the "Long-Context Tax" and exhibit weak correlation with downstream SWE performance. In this paper, we bridge this gap by first introducing a rigorous data filtering strategy. Crucially, we propose the Entropy Compression Hypothesis, redefining intelligence not by scalar Top-1 compression, but by the capacity to structure uncertainty into Entropy-Compressed States of low orders ("reasonable hesitation"). Grounded in this fine-grained entropy analysis, we formulate a novel metric, HE-SNR (High-Entropy Signal-to-Noise Ratio). We validate our approach on models with up to 560B parameters across different context windows (32K/128K). This work provides both the theoretical foundation and practical tools for optimizing the latent potential of LLMs in complex engineering domains.

翻译：SWE-bench已成为评估大型语言模型在复杂软件工程任务中能力的主要基准。尽管这些能力从根本上是在中期训练阶段获得，并在后续的监督微调（SFT）中被激发，但目前仍缺乏能够有效指导中期训练的指标。困惑度（PPL）等标准指标受“长上下文税”影响，与下游SWE性能的相关性较弱。本文通过引入一种严格的数据过滤策略来填补这一空白。关键地，我们提出了“熵压缩假设”，将智能重新定义为并非标量Top-1压缩，而是将不确定性结构化为低阶熵压缩状态（“合理犹豫”）的能力。基于这种细粒度熵分析，我们构建了一个新颖指标——HE-SNR（高熵信噪比）。我们在不同上下文窗口（32K/128K）下对参数规模高达560B的模型验证了该方法。本研究为优化LLMs在复杂工程领域的潜在能力提供了理论基础和实践工具。