aiqbench analysis
This research introduces a minimal architecture where an artificial agent's internal perspective dynamically reorganizes perceptual encoding based on prior experience, effectively creating history-dependent salience gating. The study demonstrates that only adaptive self-modulation can sustain this evolving viewpoint without compromising gross behavioral stability, offering new insights into how agents maintain consistent world models despite sensory perturbations.
Same World, Differently Given: History-Dependent Perceptual Reorganization in Artificial Agents
arXiv:2604.04637v2 Announce Type: replace 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, the perspective latent reorganizes perceptual encoding: identical observations are represented differently depending on prior experience, and this reorganization of salience gating replicates across runs, with five of 16 gating dimensions changing direction consistently across 30 independent runs after correction for multiple comparisons. Second, only adaptive self-modulation yields the characteristic growth-then-stabilization dynamic of the perspective latent, unlike rigid or always-open update regimes. Third, perturbation history is followed by reduced adaptive plasticity after nominal conditions are restored, showing a directionally consistent trend across seeds. Gross behavior remains stable throughout the analysis, 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.
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, the perspective latent reorganizes perceptual encoding: identical observations are represented differently depending on prior experience, and this reorganization of salience gating replicates across runs, with five of 16 gating dimensions changing direction consistently across 30 independent runs after correction for multiple comparisons. Second, only adaptive self-modulation yields the characteristic growth-then-stabilization dynamic of the perspective latent, unlike rigid or always-open update regimes. Third, perturbation history is followed by reduced adaptive plasticity after nominal conditions are restored, showing a directionally consistent trend across seeds. Gross behavior remains stable throughout the analysis, 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.
Artificial Intelligence (cs.AI)
arXiv:2604.04637 [cs.AI]
(or arXiv:2604.04637v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.04637 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 289 words
