Filtered to multi-agent systems · clear filter
This paper explores how agentic systems can improve on their own through co-evolution, where multiple agents and their environment adapt to each other, and discusses the challenges and benefits of building such systems that can learn beyond human design.
This paper proposes a method to optimize prompts for multi-agent systems by identifying which agent's modification resolves a failure, and then using that agent's output as supervision to extract a fine-grained gradient. Practitioners might care because it could improve the performance of large language model-based multi-agent systems.
This paper introduces a new framework called RSIAgent that helps digital agents adapt to new environments without needing to be retrained. A practitioner might care about this because it allows for more efficient and effective AI systems that can learn and improve on their own.
This paper proposes a new method to improve the performance of large language models on knowledge-intensive tasks by distilling knowledge from proprietary models and using reinforcement learning. Practitioners may care about this approach because it can help bridge the gap between proprietary and open-source models, leading to more effective and robust AI systems.