Dynamic programming over non-redundant constraints yields 4^n time for an NP-hard IA fragment and asymptotically matches the o(n)^n bound for RCC.
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2026 2verdicts
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TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.
citing papers explorer
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Towards Single Exponential Time for Temporal and Spatial Reasoning: A Study via Redundancy and Dynamic Programming
Dynamic programming over non-redundant constraints yields 4^n time for an NP-hard IA fragment and asymptotically matches the o(n)^n bound for RCC.
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TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning
TrajTok learns multi-resolution hexagonal spatial tokens from GPS data and pretrains a factorized transformer with ST-RoPE and masked modeling to yield frozen encoders that outperform task-specific methods on similarity, classification, and travel-time tasks in the Porto dataset.