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LiFT is a longitudinal instruction fine-tuning framework that unifies diverse temporal NLP tasks under a shared instruction schema with curriculum-based training. Evaluated across OLMo, LLaMA, and Qwen models, LiFT consistently outperforms base-model in-context learning, especially on out-of-distribution data and rare change events.
This paper introduces Zep, a temporal knowledge graph architecture for agent memory that outperforms MemGPT in benchmarks like DMR and LongMemEval. It highlights Zep's ability to handle dynamic knowledge integration and temporal reasoning for enterprise use cases.