Learning processes underlying representational drift

Determine which learning processes generate representational drift while preserving memory and behavior in biological or artificial learning systems.

Background

The paper frames representational drift as a change in neural population codes that can occur despite stable behavioral performance. Although the study demonstrates that continual-learning rules such as experience replay, functional distillation, and parameter anchoring produce distinct drift profiles in artificial neural networks, it does not determine which underlying learning processes generally generate drift while maintaining memory and behavior.

This problem concerns the unresolved mechanistic basis of drift, including how ongoing learning, synaptic changes, and memory-preservation mechanisms jointly produce changing internal representations without catastrophic forgetting. It is broader than the paper’s experimentally addressed comparison of continual-learning rules.

References

What remains unclear is which learning processes generate such change while preserving memory and behavior.

— Continual-learning rules shape representational drift  (2608.16141 - Si et al., 17 Aug 2026) in Introduction

Continual learning remains unclear.

— Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements  (2608.17310 - Zheng et al., 18 Aug 2026) in Appendix A.1, paragraph “Continual learning remains unclear.”