TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models 事件

TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models arXiv:2510.27544v2 Announce Type: replace Abstract: Temporal reasoning involves understanding how systems evolve over time through input-driven state transitions. A key aspect is temporal causal reasoning, causally reasoning about what prior inputs were necessary in causing an observed outcome. While large language models (LLMs) perform well at forward simulation, predicting outputs from inputs, they struggle to identify