# Evolution without an Oracle: Driving Effective Evolution with LLM Judges

arXiv: https://arxiv.org/abs/2511.19489 · HTML: https://mlsanity.com/paper/2511.19489/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [Grading Scale Impact on LLM-as-a-Judge: Human-LLM Alignment Is Highest on 0-5 Grading Scale](https://mlsanity.com/paper/2601.03444/) (2026)
- [Candidate supply and answer selection shape the value of LLM judging in multi-agent systems](https://mlsanity.com/paper/2608.25937/) (2026)
- [Reward-Free Evolving Agents via Pairwise Validator](https://mlsanity.com/paper/2607.14408/) (2026)

## Abstract (arXiv)

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.