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cs.AI2026
Efficient Test-time Inference for Generative Planning Models with OCL Search
Robert Gieselmann, Mihai Samson, Federico Pecora +1
Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One approach is to improve gener…
cs.AI2026
Self-Improvement for Fast, High-Quality Plan Generation
Robert Gieselmann, Henrike von Huelsen, Mihai Samson +9
Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality sol…
cs.AI2025
Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents
Bo Fu, Zhe Chen, Rahul Chandan +4
We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a…