ALTER: An Allen's Algebra-Based Evaluation Framework for Temporal Reasoning of LLMs

Despite the critical role of temporal reasoning in Large Language Models (LLMs), current synthetic benchmarks fall short: they either introduce information leakage or, upon addressing this issue, merely assess questions with explicit time points, failing to rigorously evaluate implicit temporal reasoning capabilities. To bridge this gap, we propose a formally grounded evaluation framework, ALTER, based on Allen’s Interval Algebra. By combining basic relations into complex networks and converting these structures into natural language forms through an automated pipeline, we construct a validated benchmark of 2,500 question-answer pairs, ensuring a controllable and scalable evaluation environment. We conduct experiments on a representative selection of LLMs. Empirical evaluations on diverse LLMs yield three key findings: (1) identifying performance decline of LLMs in highly complex scenarios involving dense events; (2) isolating complex logical interactions—rather than linguistic comprehension—as an unresolved bottleneck; and (3) revealing a critical evaluation bias induced by narrative chronology, with LLMs exploiting textual shortcuts. In summary, these findings reveal the blind spots of current evaluation paradigms, providing a rigorous foundation for the robust evaluation of complex temporal reasoning.