RankEvolve: AI Discovers Better Search Algorithms Through Evolution

📄 RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution

🎯 What This Research Is About

This groundbreaking research explores whether large language models can automatically discover improved search algorithms. Traditional retrieval methods like BM25 have relied heavily on manual parameter tuning and human expertise. RankEvolve changes the game by using evolutionary search techniques guided by LLMs to automatically generate and test new ranking algorithms.

The system represents ranking algorithms as executable code that can be iteratively mutated, recombined, and evaluated based on actual retrieval performance across multiple datasets. Starting from two proven algorithms (BM25 and query likelihood with Dirichlet smoothing), RankEvolve evolves entirely new approaches that show superior performance.

💡 Why This Matters

• Automation of Algorithm Discovery: Instead of relying on human intuition and manual tuning, AI can now discover novel retrieval algorithms automatically, potentially accelerating innovation in information retrieval.
• Strong Empirical Results: The evolved algorithms demonstrate promising performance across BEIR and BRIGHT benchmarks, as well as TREC DL datasets, showing real-world applicability.
• New Research Paradigm: This work opens up evaluator-guided LLM program evolution as a practical approach for discovering not just retrieval algorithms, but potentially other types of algorithms and systems.
• Practical Impact: Better retrieval algorithms mean more relevant search results, which directly improves user experience across search engines, recommendation systems, and information retrieval applications.

🔬 Technical Approach

RankEvolve builds on AlphaEvolve principles, treating ranking algorithms as programs that can evolve. The system:

• Tests candidates across 12 diverse IR datasets from BEIR and BRIGHT
• Uses retrieval performance as the selection criterion for evolution
• Generates novel, interpretable algorithms that can be analyzed and understood
• Demonstrates strong transfer capabilities to unseen test collections

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