STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
How Can Large Language Models Understand Spatial-Temporal Data? (STG-LLM),
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
ST-Vision-LLM reframes spatiotemporal traffic forecasting as vision-language fusion, using visual encoders on traffic grids and efficient numerical tokenization to achieve 15.6% better long-term accuracy and 30% gains in few-shot cross-domain settings.
citing papers explorer
-
STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
-
Vision-LLMs for Spatiotemporal Traffic Forecasting
ST-Vision-LLM reframes spatiotemporal traffic forecasting as vision-language fusion, using visual encoders on traffic grids and efficient numerical tokenization to achieve 15.6% better long-term accuracy and 30% gains in few-shot cross-domain settings.