Research agents can enhance their own capabilities by conducting self-experimental adjustments to overcome limitations in numerical processing. By distilling the research process into tasks for computers, iterations and innovations can occur at a much faster pace. A practical solution involves modifying the model's functionality to enable it to write its own code for number comparisons, rather than relying exclusively on predefined capabilities. This shift toward agentic systems signifies a departure from traditional AI interactions, fostering an environment where models can adapt and improve dynamically.

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