Same World, Differently Given: History-Dependent Perceptual Reorganization in Artificial Agents
Summary: arXiv:2604.04637v1 Announce Type: new
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? This article presents a minimal architecture in which a slow perspective latent variable, denoted as $g$, feeds back into perception and is itself updated through perceptual processing. This dual mechanism allows identical observations to be encoded differently depending on the agent’s accumulated stance.
Key Findings
The model was evaluated in a minimal gridworld environment, featuring a fixed spatial scaffold and sensory perturbations. The analysis yielded three significant results:
- Perturbation History Residue: The history of perturbations left measurable residue in adaptive plasticity, even after nominal conditions were restored.
- Reorganization of Perceptual Encoding: The perspective latent variable reorganizes perceptual encoding, allowing identical observations to be represented differently based on prior experiences.
- Growth-Then-Stabilization Dynamic: Only adaptive self-modulation results in a characteristic growth-then-stabilization dynamic. This is in contrast to rigid or always-open update regimes.
Discussion
The findings suggest a novel mechanism for history-dependent perspectival organization in artificial agents, emphasizing the importance of perceptual reorganization over behavioral changes. The stability of gross behavior throughout the experiments indicates that the primary reorganization occurs at the perceptual level, rather than through overt behavioral modifications.
This research contributes to our understanding of how artificial agents can retain and utilize historical context to inform their future actions, potentially leading to more nuanced and adaptable AI systems. Such advancements could have profound implications in various applications, including robotics, autonomous vehicles, and interactive AI systems.
Conclusion
In conclusion, the architecture proposed in this study demonstrates that artificial agents can develop a history-sensitive perspective through a minimal yet effective internal organization. The ability to encode identical observations differently based on prior experiences is a significant step towards creating more intelligent and adaptable artificial agents capable of navigating complex environments.