# Local Guidance for Configuration-Based Multi-Agent Pathfinding

arXiv: https://arxiv.org/abs/2510.19072 · HTML: https://mlsanity.com/paper/2510.19072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [Iterative Refinement for Real-Time Multi-Robot Path Planning](https://mlsanity.com/paper/2102.12331/) (2021)
- [Multi-Robot Path Deconfliction through Prioritization by Path Prospects](https://mlsanity.com/paper/1908.02361/) (2019)
- [Graph Attention-Guided Search for Dense Multi-Agent Pathfinding](https://mlsanity.com/paper/2510.17382/) (2025)
- [Anytime Multi-Agent Path Finding using Operation Parallelism in Large Neighborhood Search](https://mlsanity.com/paper/2402.01961/) (2024)
- [Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.12623/) (2025)

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

- [Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding](https://mlsanity.com/paper/2609.32019/) (2026)

## Abstract (arXiv)

Guidance is an emerging concept that improves the empirical performance of real-time, sub-optimal multi-agent pathfinding (MAPF) methods. It offers additional information to MAPF algorithms to mitigate congestion on a global scale by considering the collective behavior of all agents across the entire workspace. This global perspective helps reduce agents' waiting times, thereby improving overall coordination efficiency. In contrast, this study explores an alternative approach: providing local guidance in the vicinity of each agent. While such localized methods involve recomputation as agents move and may appear computationally demanding, we empirically demonstrate that supplying informative spatiotemporal cues to the planner can significantly improve solution quality without exceeding a moderate time budget. When applied to LaCAM, a leading configuration-based solver, this form of guidance establishes a new performance frontier for MAPF.