A multi-agent prompt optimization system using environment feedback improves LLM agent success rates on BabyAI tasks from 0% to 72.5% on challenging coordination tasks.
arXiv preprint arXiv:2406.11132 , year=
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The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
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Environment-Grounded Automated Prompt Optimization for LLM Game Agents
A multi-agent prompt optimization system using environment feedback improves LLM agent success rates on BabyAI tasks from 0% to 72.5% on challenging coordination tasks.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.