✨Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs

📝 Summary:
EvoSynth is a new framework that autonomously engineers and evolves novel, code-based jailbreak methods for LLMs, moving beyond prompt refinement. It uses self-correction to create diverse and highly successful attacks, achieving 85.5% ASR against robust models.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12710
• PDF: https://arxiv.org/pdf/2511.12710

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#LLMs #JailbreakAttacks #AISecurity #EvolutionaryAlgorithms #AIResearch

✨GigaEvo: An Open Source Optimization Framework Powered By LLMs And Evolution Algorithms

📝 Summary:
GigaEvo is an open-source framework for LLM-guided evolutionary computation, providing modular tools for complex optimization. It enhances reproducibility of AlphaEvolve-inspired methods with detailed implementations, validated on challenging problems like Heilbronn triangle placement.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17592
• PDF: https://arxiv.org/pdf/2511.17592
• Project Page: https://airi-institute.github.io/gigaevo-cover/
• Github: https://github.com/FusionBrainLab/gigaevo-core

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#LLM #EvolutionaryAlgorithms #Optimization #OpenSource #AI

✨EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience

📝 Summary:
EvoCUA introduces an evolutionary computer-use agent that combines autonomous task generation with policy optimization. This scalable approach achieves a new state-of-the-art 56.7% success rate on the OSWorld benchmark, demonstrating a robust path for advancing native agent capabilities.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15876
• PDF: https://arxiv.org/pdf/2601.15876
• Github: https://github.com/meituan/EvoCUA

🔹 Models citing this paper:
• https://huggingface.co/meituan/EvoCUA-32B-20260105
• https://huggingface.co/meituan/EvoCUA-8B-20260105

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#AI #Agents #MachineLearning #ReinforcementLearning #EvolutionaryAlgorithms

✨EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization

📝 Summary:
EvolVE improves LLM-based Verilog generation and optimization through evolutionary search. It uses MCTS for correctness and IGR for optimization, accelerated by STG. EvolVE achieves state-of-the-art performance and reduces PPA on industry-scale designs.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18067
• PDF: https://arxiv.org/pdf/2601.18067
• Github: https://github.com/weiber2002/ICRTL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/weiber2002/ICRTL

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#LLM #Verilog #EvolutionaryAlgorithms #HardwareDesign #AI

✨What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search

📝 Summary:
LLM-guided evolutionary search shows that optimization success depends on search trajectory characteristics rather than initial problem-solving ability alone, with strong optimizers refining locally w...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19440
• PDF: https://arxiv.org/pdf/2604.19440
• Project Page: https://xinhao-zhang.github.io/traj_evo_search/
• Github: https://github.com/XINHAO-ZHANG/LLMEvo_Eval

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#LLM #Optimization #EvolutionaryAlgorithms #AI #MachineLearning