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This paper proposes a multimodal AI agent that checks its own work before finishing a task. The idea is to check each step using different types of verification, and if something looks wrong, revise that part instead of starting the whole task again. The framework combines cross-modal checks, a second-model critique, rule-based checks, and tool based verification.

I liked that the paper focuses on verification as part of the agent itself, rather than treating it as something done only after the task is finished. The step-by-step correction is also a nice touch, since it could avoid having to redo an entire task when only one part went wrong.

Major issues

• The biggest issue is that the framework has not actually been experimentally evaluated yet. The paper lays out how the framework should be evaluated, but doesn't show results from running it.

Minor issues

• Some of the figures show expected trends rather than real results, so making this distinction even clearer would help avoid confusion.

• A small example showing the agent making a mistake, catching it, and correcting it would make the proposed workflow much easier to follow.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.