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Rethinking data work through a reparative justice lens reveals that accountability, not just algorithms, should be at the heart of AI safety efforts.
IA-RAG reveals that modeling knowledge as dynamic time intervals can drastically improve temporal reasoning in language models, outperforming traditional static approaches.
Personalized agents can now maintain consistent historical awareness without performance bottlenecks, thanks to a hierarchical memory system that dynamically prioritizes user-relevant information.
