Avoiding Destructive Interference in Adaptive Systems

This paper offers a theoretical, cross-domain look at how continually adaptive systems avoid destructive interference while staying functionally coherent over time. It draws parallels between artificial neural networks, where the problem appears as catastrophic forgetting, and embodied cognitive systems, where similar failures show up as experiential interference or affective overload. The analysis highlights recurring organizational strategies such as replay-like reactivation, constraint-mediated plasticity, reducing representational or experiential load, and higher-order regulation of learning dynamics. The result is a non-reductive framework for comparing structures across domains, with implications for continual learning, embodied cognition, adaptive system design, agency, and long-horizon alignment.

What the study examined

This work explores how systems that keep adapting over time manage a common risk: new changes undermining useful prior behavior, a problem broadly called destructive interference. The paper looks at this problem in two areas: artificial neural networks, where it is known as catastrophic forgetting, and embodied cognitive systems, where similar breakdowns are described as experiential interference or affective overload.

Rather than treating these areas as unrelated, the analysis compares their structures and responses. The goal is to find recurring organizational strategies that appear when a system must remain adaptive without losing coherence across time.

Key findings

• Replay-like reactivation: Systems tend to reuse or reawaken prior states or patterns as a way to preserve earlier capabilities while taking on new information.
• Constraint-mediated plasticity: Adaptation is often limited by constraints that guide change—allowing learning where it is safe and limiting it where prior functions must be preserved.
• Load reduction: Lowering representational or experiential load—making what is stored or attended to simpler or sparser—helps prevent interference among competing demands.
• Higher-order regulation of learning: Meta-level control over how and when adaptation happens appears repeatedly as an organizing principle to balance stability and flexibility.

The paper frames these strategies as recurring organizational answers to sustained adaptive pressure, applicable across different kinds of systems without claiming they share the same underlying causes.

Why it matters

By offering a non-reductive framework for structural comparison, the analysis connects insights from machine learning and embodied cognition. This can inform thinking about continual learning, system design that must remain reliable over long periods, and questions about adaptive agency.

The work also points toward broader considerations for long-horizon alignment—how systems can stay coherent and safe as they continue to change—while avoiding metaphysical or normative claims about what these systems fundamentally are.