Generalization of Algorithmic Behaviors in Implicit Amortization Models

Determine how the algorithmic behaviors observed under specific assumptions—such as gradient descent and causal discovery—generalize beyond those specialized setups in implicit amortization models, namely transformer-based in-context learners and prior-fitted networks where a trainable predictive function f_γ processes both the query and the observation set while g is the identity map or a subsampling mechanism.

Background

Implicit amortization refers to models with a trainable predictive function that jointly internalizes task-invariant mechanisms and task adaptation through forward-pass conditioning, typically by feeding both the query and the set of observations directly into the model (e.g., in-context learning and prior-fitted networks). In this setting, g is usually the identity or a subsampling mechanism, and task-specific parameters are not explicitly exposed.

Prior work has shown that, under specific assumptions, such implicit models can recover algorithmic behaviors (for example, behaving like gradient descent during in-context learning or performing causal discovery). The paper notes that it remains unclear how these findings extend to broader settings. Clarifying the scope and conditions under which these algorithmic behaviors persist would deepen understanding of implicit amortization and its theoretical guarantees beyond viewing these models as learning posterior predictive distributions.