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Even state-of-the-art MLLMs fail to grasp intuitive visual reasoning, scoring less than 60% on tasks humans solve effortlessly at a glance.
Continual Search is introduced, an iterative framework that nudges the judge, over successive turns, to keep searching for unresolved diagnostic evidence within multiple benchmark suites and model families, demonstrating that effective search supersedes raw model scale.
Identifying the exact source of agent failures could revolutionize how we approach system repairs, shifting from vague outcomes to precise interventions.
