<ul><li>Large Language Models (LLMs) lack robust temporal intelligence, struggling with integrating past reasoning with future predictions and generations.</li><li>Existing methods focus on isolated temporal skills and exhibit poor generalization beyond their knowledge cutoff or requiring creative foresight.</li><li>The Time-R1 framework is introduced to empower a moderate-sized LLM with comprehensive temporal abilities including understanding, prediction, and creative generation.</li><li>Time-R1 outperforms much larger models on future event prediction and creative scenario generation benchmarks, showcasing the effectiveness of engineered reinforcement learning fine-tuning for superior temporal performance in smaller models.</li></ul>

Time-R1: Towards Comprehensive Temporal Reasoning in LLMs

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