Imagine an AI that not only grades translations but also pinpoints exactly where they go wrong, like a super-powered language tutor. That's the promise of MQM-APE, a new framework designed to make Large Language Models (LLMs) even better at evaluating machine translation. Current LLMs, while impressive at judging overall quality, sometimes struggle to identify specific errors that truly matter to human readers. MQM-APE tackles this by having the LLM play three roles: evaluator, editor, and verifier. First, it acts as an evaluator, using the established MQM (Multidimensional Quality Metric) framework to flag potential errors. Then, it switches hats to become an editor, attempting to correct those errors. Finally, the LLM becomes a verifier, judging whether the 'edited' translation is actually better than the original. This process cleverly filters out 'noise' – errors that don't really impact understanding. What's left are high-quality error annotations that pinpoint the most impactful flaws. The impact of MQM-APE? More reliable translation scores that better align with human judgment, and clearer, more useful feedback for improving translations. Plus, this framework isn't tied to specific LLMs or languages; it's shown to be effective across a diverse range, including low-resource languages like Assamese and Maithili. The results are compelling: significantly improved accuracy in identifying system-level translation rankings, better segment-level evaluations, and higher quality error spans. This innovative approach also shines a light on the varying strengths and weaknesses of different LLMs as translation evaluators. While MQM-APE adds a bit of extra processing time, the researchers suggest smart workarounds like replacing the 'verifier' role with existing metrics, potentially speeding things up. The ability to precisely pinpoint critical errors opens doors for better automatic post-editing and more effective human feedback. This represents a significant step towards more transparent, human-like AI translation evaluation. Future work will explore further cost reduction strategies and address the challenge of aligning the distribution of errors to human-annotated evaluations.