Recursive Agents implements a three-phase iterative refinement architecture where LLM agents (instances of Classes) critique and improve their own outputs. Unlike single-pass systems, each agent ...
Abstract: For any given target trajectory, asymptotic tracking error convergence can be achieved as the number of iterations tends to infinity by applying existing ...
Abstract: Using the proportional-type update rule (PTUR) is the most common update approach for iterative learning control. By combining PTUR and a newly proposed fractional-power-type update rule ...
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