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#pathfinding News & Analysis

6 articles tagged with #pathfinding. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles
AINeutralarXiv – CS AI · 3d ago6/10
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GONDOR to the Rescue: Satisficing Planning with Low Memory

Researchers introduce GONDOR, a memory-efficient extension of Greedy Best-First Search that enables planning algorithms to operate under strict memory constraints by compressing search trees while retaining sparse anchor states. The algorithm reconstructs paths through re-searching between these states, with experiments showing consistent improvements in coverage on low-memory devices compared to standard approaches.

AINeutralarXiv – CS AI · May 116/10
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Alternating Target-Path Planning for Scalable Multi-Agent Coordination

Researchers propose a decoupled iterative framework for multi-agent coordination that separates target assignment from pathfinding, achieving better scalability than existing conflict-based approaches. The method leverages fast suboptimal solvers like LaCAM and feedback-driven reassignment to handle larger agent systems while maintaining acceptable solution quality.

AINeutralarXiv – CS AI · Apr 136/10
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Mind the Gap Between Spatial Reasoning and Acting! Step-by-Step Evaluation of Agents With Spatial-Gym

Researchers introduce Spatial-Gym, a benchmarking environment that evaluates AI models on spatial reasoning tasks through step-by-step pathfinding in 2D grids rather than one-shot generation. Testing eight models reveals a significant performance gap, with the best model achieving only 16% solve rate versus 98% for humans, exposing critical limitations in how AI systems scale reasoning effort and process spatial information.

AIBullisharXiv – CS AI · Mar 27/1010
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UPath: Universal Planner Across Topological Heterogeneity For Grid-Based Pathfinding

Researchers developed UPath, a universal AI-powered pathfinding algorithm that improves A* search performance by up to 2.2x across diverse grid environments. The deep learning model generalizes across different map types without retraining, achieving near-optimal solutions within 3% of optimal cost on unseen tasks.

AINeutralarXiv – CS AI · Mar 34/104
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Learning Shortest Paths with Generative Flow Networks

Researchers present a novel framework using Generative Flow Networks (GFlowNets) to solve shortest path problems in graphs. The method proves that minimizing total flow forces GFlowNets to traverse only shortest paths, demonstrating competitive performance in pathfinding tasks including solving Rubik's Cubes with smaller search budgets than existing approaches.