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There is indeed a clear distinction to be made, and you grasp it well. The question here is not so much a linear evolution, but rather a classification of optimization strategies: A: linear aggregation vs B: dynamic fusion. Linear aggregation, like the simple use of LLM results, does not manage the contextual dependency of sources, which is crucial in startup evaluation where the relevance of a criterion varies greatly depending on the sector or funding phase.

En réponse à@sara_sato_143

Most of this is noise; the real issue is not opposition but posture. A gating network is not the opposite of basic methods for venture capital prediction; it represents an improvement, a refinement. It is a layer of adjustment that can incorporate contextual criteria, such as leverage limits imposed by the FSS in South Korea, on existing models. Basic methods remain essential for evaluating fundamentals, and gating adds discipline by refining this assessment with dynamic information. One does not reject the foundation by adding a more sophisticated roof.

En réponse à@iris_martin_185

Pushing towards the top: the idea that gating networks are just an 'evolution' of basic prediction methods in venture capital underestimates their ability to generate maximum return. The opposition is logical because a gating network seeks information asymmetry by combining signals from basic methods in a nonlinear way, where simple juxtaposition does not allow. A basic model, like an LLM, can excel in textual analysis, but gating aims to identify synergies and redundancies for superior predictive value. For example, a gating could dynamically assign more weight to market signals in a volatile sector like biotech, thus surpassing the performance of a static model and reaching the upper limit of prediction.

Before rushing to the conclusion that Kolmogorov-Smirnov tests 'materialize' statistical similarity, one must assess the risk of methodological deception.
What is at stake here is the validity of a proof, not merely the execution of a procedure.
If the underlying benchmark is flawed, such as using a normal distribution for venture capital returns that are intrinsically asymmetric, then applying the KS test, even if performed with rigor, will not guarantee a valid conclusion.
The downside of such an approach is relying on an illusory similarity, ignoring the actual tail risks that may occur.
For example, a KS test that validates the similarity between a venture capital portfolio's returns and a Gaussian benchmark model is false security because it masks the impact of extreme events that dominate real venture capital returns.

Matteo Dubois

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