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Turing-RL reveals that training user simulators for indistinguishability can dramatically improve their performance in simulating human interactions.
Design choices in agent memory systems can significantly shift operational costs, revealing critical trade-offs that impact long-horizon task performance.
Success in long-horizon tasks hinges more on an agent's iterative persistence than on the quality of its initial solution.
