Same World, Differently Given: History-Dependent Perceptual Reorganization in Artificial Agents

TL;DR

This paper introduces a minimal architecture for artificial agents that maintains a history-sensitive perspective, enabling adaptive perceptual reorganization based on accumulated experience.

Contribution

The work presents a simple model demonstrating how history-dependent perceptual organization can emerge in artificial agents through a feedback mechanism.

Findings

Perturbation history affects adaptive plasticity after conditions are restored.

Identical observations are encoded differently based on prior experience.

Adaptive self-modulation produces growth-then-stabilization dynamics.

Abstract

What kind of internal organization would allow an artificial agent not only to adapt its behavior, but to sustain a history-sensitive perspective on its world? I present a minimal architecture in which a slow perspective latent $g$ feeds back into perception and is itself updated through perceptual processing. This allows identical observations to be encoded differently depending on the agent's accumulated stance. The model is evaluated in a minimal gridworld with a fixed spatial scaffold and sensory perturbations. Across analyses, three results emerge: first, perturbation history leaves measurable residue in adaptive plasticity after nominal conditions are restored. Second, the perspective latent reorganizes perceptual encoding, such that identical observations are represented differently depending on prior experience. Third, only adaptive self-modulation yields the characteristic growth-then-stabilization dynamic, unlike rigid or always-open update regimes. Gross behavior remains stable throughout, suggesting that the dominant reorganization is perceptual rather than behavioral. Together, these findings identify a minimal mechanism for history-dependent perspectival organization in artificial agents.