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Experiments show that turn-level boundary supervision improves intervention localization, while reinforcement learning further improves the safety--utility trade-off.
This work forms attack and defense as partially observed state control in SEAD, deriving their design requirements from this shared execution process and constructs an environment-verifiable dataset integrating controlled initial states, replayable tool environments, and task-specific executable checks.
AcFlow is introduced, an inference-time controller that transports intermediate layer image-token activations through a learned concept-conditioned velocity field while keeping the base DiT frozen, and supports the learned velocity field as an adaptive control mechanism.
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