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Today's best language models can barely make sense of your messy group chats and fragmented digital life, achieving only 19% accuracy on a new benchmark of real-world reasoning.
RAG models struggle to ignore their pre-trained knowledge, even when it contradicts the provided context, but a new dataset can help them learn to be more faithful.
Multi-turn reinforcement learning gets a boost: weighting trajectories by semantic similarity dramatically improves baseline estimation and agent performance in long-document visual QA.
