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)
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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.
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.
citing papers explorer
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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.
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A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.