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Loreley: Repository-Scale Program Evolution with Quality-Diversity Search

arXiv 2608.19703 ↗First posted 20 Aug 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

Sequential agent search accumulates changes from its current champion but discards alternative branches; independent proposals preserve breadth but restart from the root. Loreley instead retains complete repository states in a Quality-Diversity (QD) archive and samples them as parents or supplies them as context for later edits. Candidates are Git commits produced in isolated worktrees and judged by a project-supplied evaluator. We compare configured Loreley QD, sequential champion editing, and independent root proposals in a matched Zstandard experiment: seven paired blocks and 48 physical candidate jobs per policy and block (1,008 total), with root-only initialization and each policy's native concurrency. Validation selected a winner at each budget checkpoint; an agent-hidden holdout measured the fixed candidate. At 48 jobs, QD was 0.135% below Sequential Champion (95% BCa interval for the paired effect: -0.556% to +0.161%) and 0.320% above Independent Root (-0.082% to +0.686%). Neither contrast established a QD advantage; Sequential had the highest observed 48-job mean and median. Archive retention and later sampling did occur. Four of seven final QD winners had a non-incumbent state in their primary-parent ancestry under a retrospective one-incumbent rule applied only to the observed QD stream. Including inspiration edges raised the count to six, without showing that supplied context caused an edit. Three earlier capability campaigns produced generation-4, multi-file improvements in two Python libraries and a separate Zstandard revision. Loreley engaged the intended stepping-stone mechanism, but the controlled experiment did not show an endpoint benefit at 48 jobs.

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References · 18

  1. 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 867
  2. 2015Illuminating search spaces by mapping elitescited by 425
  3. 2022Evolution through Large Modelscited by 134
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  5. 2025CodeEvolve: an open source evolutionary coding agent for algorithmic discovery and optimizationcited by 44
  6. 2026RHO: Your Coding Agent is Secretly a Roboticistcited by 20
  7. 2022Multi-Objective Quality Diversity Optimizationcited by 19
  8. 2025Autonomous Code Evolution Meets NP-Completenesscited by 15
  9. 2024Quality-Diversity Algorithms Can Provably Be Helpful for Optimizationcited by 9
  10. 2025EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaborationcited by 9
  11. 2026Simple Baselines are Competitive with Code Evolutioncited by 9
  12. 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 8
6 more
  1. 2026Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABCcited by 6
  2. 2026Effective Harness Engineering for Algorithm Discovery with Coding Agentscited by 6
  3. 2026GEAR: Genetic AutoResearch for Agentic Code Evolutioncited by 4
  4. 2026What Do Evolutionary Coding Agents Evolve?cited by 3
  5. 2026Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Noveltycited by 2
  6. 2026QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Designcited by 1

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