\begin{table*}[tb]
\caption{Architectural differences from modular grounding and trained GRES methods. This is not a shared-training performance comparison. Query counts refer to distinct language inputs, not transformer object slots.}\label{tab:novelty}
\centering\small\setlength{\tabcolsep}{4pt}
\begin{tabular}{p{30mm}p{27mm}p{26mm}p{28mm}p{32mm}}
\toprule Property & MAttNet \cite{mattnet} & ReLA \cite{gres} & InstAlign \cite{phrasealign} & SFAP\\\midrule
Task-specific training & Yes & Yes & Yes & No\\
Target/anchor structure & Subject, location, relation modules & Region--language attention & Learned phrase--instance alignment & Explicit text decomposition\\
Relation decision & Learned relational score & Learned dependencies & Learned alignment & Fixed centroid filter\\
Ambiguity fallback & No SFAP-style fallback & No SFAP-style fallback & No SFAP-style fallback & Original full-query mask\\
Plurality & Single referent & Generalized mask & Instance aggregation & Component union\\
Empty targets & No dedicated GRES rule & No-target head & Relevance/head & Native emptiness; action rule\\
Language queries & One & One & One & One to three\\
Weight updates & In training & In training & In training & None\\\bottomrule
\end{tabular}
\end{table*}