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This work introduces ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it, separating test construction from source-code repair.
AppSim-Bench is proposed, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation and removes major sources of environmental stochasticity, enabling reproducible cross-model comparison.
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