US EditionMIT and NUS Researchers Introduce MEM1: A Memory-Efficient Framework for Long-Horizon Language Agents
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MIT and NUS Researchers Introduce MEM1: A Memory-Efficient Framework for Long-Horizon Language Agents
Modern language agents need to handle multi-turn conversations, retrieving and updating information as tasks evolve. However, most current systems simply add all past interactions to the prompt, regardless of relevance. This leads to bloated memory usage, slower performance, and poor reasoning on longer inputs that weren’t seen during training. Real-world examples, such as research or shopping assistants, show how follow-up questions depend on t…
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