Extent to which FER causes core AI challenges

Ascertain the degree to which fractured entangled representations in contemporary foundation models contribute to challenges including sample inefficiency, lack of reliability, hallucination, poor out-of-distribution generalization, idiosyncratic failures on simple tasks, and poor continual learning, and determine whether FER constitutes a fundamental barrier to progress in modern AI.

Background

The authors argue that FER can degrade generalization, creativity, and learning, potentially undermining key capacities of large models. They enumerate current model shortcomings (e.g., sample inefficiency, unreliability, hallucination, OOD brittleness, simple-task failures, poor continual learning) and posit that FER may be a root cause.

This open question calls for rigorous causal analyses linking representational pathology (FER) to observed performance deficits across tasks and settings, especially at knowledge frontiers where data is sparse.