# LLM-Advisor: An LLM Advisor for Cost-efficient Path Planning across Multiple Terrains

arXiv: https://arxiv.org/abs/2503.01236 · HTML: https://mlsanity.com/paper/2503.01236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning](https://mlsanity.com/paper/2310.10103/) (2023)
- [Path Planning using Neural A* Search](https://mlsanity.com/paper/2009.07476/) (2020)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2407.02511/) (2024)
- [Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?](https://mlsanity.com/paper/2412.09743/) (2024)
- [Traversability-aware Adaptive Optimization for Path Planning and Control in Mountainous Terrain](https://mlsanity.com/paper/2404.03274/) (2024)
- [HAS-RRT: RRT-based Motion Planning using Topological Guidance](https://mlsanity.com/paper/2309.10801/) (2023)
- [FRTree Planner: Robot Navigation in Cluttered and Unknown Environments with Tree of Free Regions](https://mlsanity.com/paper/2410.20230/) (2024)

## Built on by (most cited first, 1 of 1)

- [CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query](https://mlsanity.com/paper/2510.07516/) (2025)

## Abstract (arXiv)

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.