Temporal Reasoning in Language Models

Temporal reasoning asks whether language models can track when events happen, how states change, and which facts apply at a particular time. It matters for tasks such as answering questions about evolving knowledge, reconstructing long conversations, and interpreting clinical or legal timelines. Common approaches include temporal knowledge graphs and versioned memories, retrieval-augmented generation, executable rules for dates and intervals, and training or steering models to respect temporal cues. Benchmarks test date and event ordering, multi-turn memory, time-stamped evidence, and temporal patterns in video or time series; a central challenge is that retrieving a fact does not ensure the model knows whether it is current, historical, or valid for the question’s time frame.