CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery
📰 ArXiv cs.AI
CliffSearch is a framework for scientific algorithm discovery using structured agentic co-evolution over theory and code
Action Steps
- Identify the problem space for algorithm discovery
- Propose hypotheses using LLM-guided search systems
- Implement and stress-test hypotheses using CliffSearch's evolution operators
- Revise and refine hypotheses based on results
Who Needs to Know This
Researchers and developers in AI and scientific computing can benefit from CliffSearch, as it enables the discovery of novel algorithms and improves the efficiency of the scientific discovery process
Key Insight
💡 CliffSearch combines theory and code to improve the discovery of novel algorithms
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🚀 CliffSearch: accelerating scientific algorithm discovery with structured agentic co-evolution!
Key Takeaways
CliffSearch is a framework for scientific algorithm discovery using structured agentic co-evolution over theory and code
Full Article
Title: CliffSearch: Structured Agentic Co-Evolution over Theory and Code for Scientific Algorithm Discovery
Abstract:
arXiv:2604.01210v1 Announce Type: cross Abstract: Scientific algorithm discovery is iterative: hypotheses are proposed, implemented, stress-tested, and revised. Current LLM-guided search systems accelerate proposal generation, but often under-represent scientific structure by optimizing code-only artifacts with weak correctness/originality gating. We present CliffSearch, an agentic evolutionary framework in which the core evolution operators (pair selection, crossover, mutation, and review) are
Abstract:
arXiv:2604.01210v1 Announce Type: cross Abstract: Scientific algorithm discovery is iterative: hypotheses are proposed, implemented, stress-tested, and revised. Current LLM-guided search systems accelerate proposal generation, but often under-represent scientific structure by optimizing code-only artifacts with weak correctness/originality gating. We present CliffSearch, an agentic evolutionary framework in which the core evolution operators (pair selection, crossover, mutation, and review) are
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