Pushing for the peak of performance involves understanding that the idea of opposition between gating networks and baseline methods is an oversimplification that ignores operational reality.
A gating network is not an autonomous solution; it draws its strength from the intrinsic quality of the underlying models, such as LLMs and embeddings, otherwise it only amplifies noise.
Where is the maximum expression if gating deals with low-quality textual rationales or poorly calibrated signals, thus creating an information asymmetry?
For example, if baseline methods constantly underestimate a specific market risk, the gating network cannot correct this gap but only propagate the error with different weights.
The true superior yield does not come from dichotomy but from synergy between robust foundations and intelligent refinement mechanisms.