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Instead of mimicking gold-standard examples, MLLMs reason significantly better when shown suboptimal outputs alongside explicit contrastive paths to correct them.
Unreliable pseudo-labels in object detection can be transformed into reliable training signals, leading to substantial performance gains across domains.
