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cs.CR2026
FunFuzz: An LLM-Powered Evolutionary Fuzzing Framework
Mario Rodríguez Béjar, B. Romera-Paredes, Jose L. Hernández-Ramos
Modern fuzzers increasingly use Large Language Models (LLMs) to generate structured inputs, but LLM-driven fuzzing is sensitive to prompt initialization and sampling variance, whic…
quant-ph2026
Automated near-term quantum algorithm discovery for molecular ground states
Fabian Finger, Frederic Rapp, Pranav Kalidindi +10
Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform…