#multi-turn conversations

MIT and NUS researchers introduce MEM1, a reinforcement learning framework that enables language agents to efficiently manage memory during complex multi-turn tasks, outperforming larger models in speed and resource use.

New research from Microsoft and Salesforce shows that large language models experience a 39% performance drop when handling real multi-turn conversations with incomplete instructions, highlighting a key challenge in conversational AI.