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

2026-05-13 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

DiffLNS is a novel hybrid framework that integrates a discrete denoising diffusion probabilistic model (D3PM) with the LNS2 solver to address Multi-Agent Path Finding (MAPF) problems, particularly in dense and congested environments. The D3PM component acts as an initializer, learning spatiotemporal priors for coordinated multi-agent action trajectories from expert demonstrations using sparse social attention. This discrete diffusion model operates directly on the categorical action space, preserving MAPF action structure and sampling diverse, multimodal joint plans. These sampled plans serve as warm starts for LNS2, which then completes trajectories and resolves remaining conflicts under hard MAPF constraints. DiffLNS achieves an average success rate of 95.8% across 20 complex and congested settings, outperforming the strongest baseline by 9.6 percentage points and generalizing from 96-agent training instances to scenarios with up to 312 agents.

Key takeaway

For research scientists developing multi-agent coordination systems, you should consider integrating discrete diffusion models as initializers for repair-based solvers like LNS2. This approach significantly improves success rates in complex, congested environments and enhances scalability, allowing your solutions to generalize to larger agent populations than those used for training. Explore how learning spatiotemporal priors can provide robust warm starts for combinatorial planning problems.

Key insights

Discrete diffusion models can effectively warm-start LNS-based MAPF solvers for complex, congested multi-agent pathfinding.

Principles

• Initial plan quality is critical for repair-based solvers.
• Discrete diffusion preserves categorical action space structure.
• Sparse social attention aids multi-agent coordination learning.

Method

DiffLNS uses a D3PM with sparse social attention to generate diverse initial joint plans, then LNS2 refines these warm starts to resolve conflicts and complete trajectories under hard MAPF constraints.

In practice

• Apply D3PM for generating diverse initial plans.
• Use warm starts to improve LNS-based solver performance.
• Scale multi-agent systems with learned spatiotemporal priors.

Topics

• Multi-Agent Path Finding
• Discrete Diffusion Models (D3PM)
• Large Neighborhood Search (LNS2)
• Sparse Social Attention
• Multi-Agent Trajectory Planning

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 Artificial Intelligence.