Artificial Agent with History-Dependent Perception
A recent study presents a streamlined architecture for artificial agents that facilitates history-dependent changes in perception. This model includes a slow perspective latent that influences perception and evolves through processing, enabling the same observations to be encoded variably depending on past experiences. In experiments conducted within a gridworld featuring spatial scaffolds and sensory disturbances, findings indicate that the perspective latent reliably reorganizes perceptual encoding across multiple trials, with five out of 16 gating dimensions reversing direction in 30 separate runs. This research is available on arXiv with the ID 2604.04637.
Key facts
- arXiv ID: 2604.04637
- Model uses a slow perspective latent that feeds back into perception
- Identical observations encoded differently based on accumulated stance
- Evaluated in minimal gridworld with fixed spatial scaffold and sensory perturbations
- Five of 16 gating dimensions changed direction consistently across 30 runs
- Study focuses on adaptive self-modulation in artificial agents
- Published as arXiv preprint
- Research explores history-sensitive perspective in AI
Entities
Institutions
- arXiv