Dimensions overall score 8.0

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• KARL achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks.result 90%
• Models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark.result 85%
• KARL is Pareto-optimal on KARLBench across cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2.result 90%
• The system introduces KARLBench, a multi-capability evaluation suite spanning six distinct search regimes.method 95%
• The system uses an agentic synthesis pipeline that employs long-horizon reasoning and tool use to generate diverse, grounded, and high-quality training data.method 85%
• The system proposes a new post-training paradigm based on iterative large-batch off-policy RL that is sample efficient and robust to train-inference engine discrepancies.technical 85%
• With sufficient test-time compute, KARL surpasses the strongest closed models.result 80%
• The primary limitation is reliance on proprietary datasets which might limit applicability in contexts where such data isn't available.limitation 90%

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