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Even the most advanced LLMs stumble when asked to reason over a large, heterogeneous document corpus, achieving only 34% accuracy on the new OfficeQA Pro benchmark despite direct access to the relevant documents.
Forget Claude and GPT: KARL, a reinforcement-learning-trained enterprise search agent, achieves Pareto-optimal performance on a diverse suite of search tasks, even outperforming closed models with sufficient compute.
Forget finetuning: DSPy-driven prompt optimization boosts vision-language model performance on medical imaging tasks by up to 3400%, unlocking clinical utility without massive datasets or compute.
