arXiv Artificial Intelligence

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

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

Quick summary

arXiv:2608.19703v1 Announce Type: cross Abstract: 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

Key takeaways

  • arXiv:2608.19703v1 Announce Type: cross Abstract: 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.

Why it matters

“Loreley: Repository-Scale Program Evolution with Quality-Diversity Search” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗