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Success in long-horizon tasks hinges more on an agent's iterative persistence than on the quality of its initial solution.
Autonomous research agents are surprisingly unreliable, with existing systems hallucinating references 21% of the time and failing to align methods with code as often as 80%, but a new "Chain-of-Evidence" approach can fix this.
