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William & Mary
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MLLMs exhibit a staggering 80.22% bias towards incorrect outputs when faced with repeated UI patterns, revealing a critical flaw in their code generation capabilities.
AI/ML issue resolution is fundamentally different from traditional software maintenance, requiring new frameworks to tackle unique challenges like stochastic behavior and artifact coordination.
