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LongRCA Bench reveals that diagnosing failures in long-horizon agents requires distinct metrics for responsible roles and root causes, with a new method achieving over 50% accuracy in role attribution.
Existing multimodal systems falter in repository-level localization, with the best performance still falling short of reliable accuracy thresholds.
TACO reduces token overhead by 10% while boosting terminal agent performance by up to 4%, revolutionizing how we approach long-horizon reasoning tasks.
