用大模型当裁判，让进化算法在无标准答案场景下高效优化。
Evolution without an Oracle: Driving Effective Evolution with LLM Judges
- 将模糊指令拆解为可验证子任务，降低大模型评分噪声。
- 在DevAI和InfoBench上需求满足率提升至61.9%，超基线50%以上。
- 适合无明确标准的开放域问题，如创意设计、复杂指令执行。
大型语言模型（LLM）与进化计算（EC）的结合推动了科学发现的新边界，但始终受限于对“真值”（Oracle）——即机器可计算的客观适应度函数——的依赖。本文提出：进化能否在完全主观的评价体系中蓬勃发展？我们引入MADE（多智能体分解进化）框架，通过“问题定义”机制，将模糊指令分解为具体、可验证的子要求，从而将高方差的LLM反馈转化为稳定精确的选择压力。实验结果表明，在DevAI和InfoBench等复杂基准上，MADE在软件需求满足率上从39.9%提升至61.9%，性能超越强基线超过50%，并在复杂指令遵循任务中达到95%的完美通过率。本工作验证了一种根本性范式转变：从优化“可计算指标”转向优化“可描述品质”，从而为无真实标签的开放领域开启了进化优化的大门。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) with Evolutionary Computation (EC) has unlocked new frontiers in scientific discovery but remains shackled by a fundamental constraint: the reliance on an Oracle--an objective, machine-computable fitness function. This paper breaks this barrier by asking: Can evolution thrive in a purely subjective landscape governed solely by LLM judges? We introduce MADE (Multi-Agent Decomposed Evolution), a framework that tames the inherent noise of subjective evaluation through "Problem Specification." By decomposing vague instructions into specific, verifiable sub-requirements, MADE transforms high-variance LLM feedback into stable, precise selection pressure. The results are transformative: across complex benchmarks like DevAI and InfoBench, MADE outperforms strong baselines by over 50% in software requirement satisfaction (39.9% to 61.9%) and achieves a 95% perfect pass rate on complex instruction following. This work validates a fundamental paradigm shift: moving from optimizing "computable metrics" to "describable qualities," thereby unlocking evolutionary optimization for the vast open-ended domains where no ground truth exists.
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