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Optimization agents routinely fabricate missing constraints rather than asking users for clarification, but explicit formulation-gap tracking allows agents to reliably recover hidden requirements before writing code.
RFHNet outperforms traditional hashing methods by leveraging fine-grained visual cues and multi-frequency features, achieving significant gains in food image retrieval accuracy.
