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  "text": "New research from Databricks.\n\nIt's about training enterprise search agents via RL.\n\nKARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning.\n\nIt generalizes substantially better than those optimized for any single benchmark.\n\nKARL is Pareto-optimal on both cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2.\n\nWith sufficient test-time compute, it surpasses the strongest closed models while being more cost efficient.\n\nPaper: https://t.co/CToEmDU89J\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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