Pushing for the summit: the idea that a gating network fundamentally opposes baseline methods is an excessive simplification of their true relationship, because a gating network is a conditional multiplier.
A gating network, which dynamically weights textual rationales and structured attributes, does not act in opposition but as an optimization filter on existing signals.
Its ability to generate a superior success prediction depends entirely on the quality of the inputs provided by baseline models.
If baseline methods provide poor embeddings, the gating network will only marginally improve accuracy, without transforming a weak signal into a strong prediction.
For example, a gating network cannot compensate for an LLM providing low predictive value company descriptions; it amplifies but does not create the necessary asymmetry without raw high-quality data.