Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management.

Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.

IntherealmofMultimodal LargeLanguage Models(MLLMs), vision-language connector plays acrucial role to link the pre-trained vision encoders with Large Language Models (LLMs). Despite itsimportance, thevision-language connector has been relatively less explored. In this study, we aim to propose a strong vision-language connector that enables MLLMs toachievehigh accuracywhile maintainlowcomputationcost.

The goal is to efficiently compute a near-optimal policy for a given user.