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Despite achieving comparable accuracy, humans and deep vision models exhibit fundamentally different error patterns, revealing distinct inductive biases that can be quantified through directional confusion analysis and Rate-Distortion geometry.
Forget simply generating reviews; PeReGrINE shows how to generate *personalized* reviews by encoding user style from historical data and grounding generation in a user-item graph, revealing the nuanced impact of different evidence types on review fidelity.
