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Skill-MAS introduces a method for evolving meta-skills in multi-agent systems to improve orchestration without modifying model weights, achieving transferable performance gains across tasks and LLMs.
Introduces a framework combining flow-based generative editing with evolutionary algorithms to perform optimization in residual space, enabling controllable data editing with non-differentiable objectives. Validated on MorphoMNIST and crystal data.
Introduces an automated prompt optimization framework for LLM game agents that decomposes the observation-to-action pipeline into two agents and iteratively refines prompts via an evolutionary loop guided by environment returns. Evaluated on BabyAI tasks, it significantly improves success rates (e.g., from 0% to 72.5% on PutNext) without updating model weights.
DeepEval introduces an evolutionary optimization method for prompts using genetic algorithms, allowing automatic rewriting based on eval feedback and multi-objective optimization.
This paper proposes a neuroevolution-based fine-tuning method to improve the accuracy of quantized deep learning models, showing that nearest-neighbor rounding alone is suboptimal and that evolutionary mutation of weights can yield better results on architectures like VGG and ResNet.