Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention

2026-05-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

DiffLNS is a novel hybrid framework designed to improve Multi-Agent Path Finding (MAPF) in complex, congested environments by integrating a discrete denoising diffusion probabilistic model (D3PM) with the LNS2 repair-based solver. The D3PM acts as an initializer, learning spatiotemporal priors for coordinated multi-agent action trajectories from expert demonstrations and sampling diverse joint plans. This discrete diffusion model operates directly on the categorical action space, preserving MAPF action structure and generating multimodal joint-plan drafts. These drafts serve as "warm starts" for LNS2, which then completes unfinished trajectories and resolves remaining conflicts under hard MAPF constraints. Despite training on instances with up to 96 agents, DiffLNS generalizes to scenarios with up to 312 agents, achieving an average success rate of 95.8% across 20 complex settings, outperforming the strongest baseline by 9.6 percentage points.

Key takeaway

For research scientists developing multi-agent coordination systems, DiffLNS demonstrates that integrating generative models like D3PM for initialization significantly boosts the robustness and success rate of repair-based MAPF solvers like LNS2. You should consider exploring hybrid approaches that combine learned generative priors with classical constraint satisfaction to tackle highly congested and complex multi-agent planning challenges, especially when scalability to larger agent teams is critical.

Key insights

DiffLNS combines discrete diffusion for initial plan generation with LNS2 for robust multi-agent pathfinding repair.

Principles

• Initialization quality critically impacts LNS-based MAPF solver success.
• Discrete diffusion can generate diverse, structured joint action plans.
• Sparse social attention improves efficiency in multi-agent interaction modeling.

Method

DiffLNS samples joint action drafts via a D3PM with sparse social attention, preprocesses them, and then iteratively repairs them using LNS2, selecting the best feasible solution.

In practice

• Use D3PM to generate diverse initial plans for complex MAPF.
• Implement sparse social attention for scalable multi-agent coordination.
• Preprocess diffusion drafts to complete trajectories before LNS2 repair.

Topics

• Multi-Agent Path Finding
• Discrete Diffusion Models
• Large Neighborhood Search 2
• Sparse Social Attention
• Generative Planning

Code references

• Jiaoyang-Li/MAPF-LNS2
• marmotlab/LNS2-RL
• proroklab/hmagat
• Kei18/lacam3
• CognitiveAISystems/pogema

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.