SOURCE-LINKED INTELLIGENCE

Compact Bellman-Grounded Cognitive Maps for Cost-Aware Navigation

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Biological agents navigate familiar environments not by re-solving routes for each new goal, but by reusing a learned map built once and read off as goals change. Existing artificial cognitive-map models mimic this reuse, yet their guidance is not explicitly grounded in additive heterogeneous route costs. Furthermore, they often struggle with memory efficiency: representative state-indexed and high-rank spectral constructions incur substantial storage growth as the environment scales. We present BCM, which grounds a reusable cognitive map in local edge costs through a self-supervised Bellman-g

Read original source ↗ Open in workspace

recordType

paper

region

Global