用大模型优化地图路径，让机器人走更省钱的路。

This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while missing lower-cost terrain corridors available in the native-resolution map. We propose LLM-Advisor, an external verification-guided multimodal refinement framework that uses semantic terrain context to propose route alternatives with lower cost than the baseline route. The framework combines a multimodal prompt, map-disjoint Reference Example Augmentation (REA), and deterministic validation with fallback: a proposed route is deployed only when it is feasible and strictly lower in cost than the baseline. To evaluate the method, we introduce MultiTerraPath, a controlled 2,000-map benchmark with Easy and Hard subsets for fixed-graph terrain-cost refinement, and further conduct a semantic-cost transfer evaluation on RUGD. Using coarse-lattice A* with stride r=50 as the fixed baseline planner, we compare LLM-Advisor with direct LLM planning and LLM-A*. LLM-Advisor with GPT-5.5 achieves the highest FIR on MultiTerraPath, improving 52.00% of Easy maps and 45.00% of Hard maps. Deterministic verification and fallback yield 100.00% NDR and Final Validity across all evaluated RUGD scene groups. Across lattice strides from r=10 to r=100, offline oracle analysis shows that coarser graphs create larger coarse--oracle gaps, while LLM-Advisor recovers up to 49.16% and 29.98% of the available gap on Easy and Hard maps when r=100, respectively.