STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering
2026-08-17 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors introduce STAIR, a system that separates understanding the meaning of time-related questions from the exact calculation of time answers. Instead of relying fully on AI language models for all parts, STAIR uses clear rules and logical steps to handle precise timing, making its decisions easier to check. When questions are more complicated, a language model helps interpret them, but only after the rules have been tried. Their tests show STAIR does better than other methods on tasks involving understanding and reasoning about time in questions.
large language modelstemporal reasoningsemantic interpretationdeterministic automatontemporal inferenceguarded transitionsrule-based systemsquestion answeringinterval reasoningtemporal logic
Authors
Xinlong Dai, Jinchuan Zhang, Lei Gao, Xinzhe Hu, Yuefeng He, Hui Gao
Abstract
By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training. However, existing prompt-based neuro-symbolic systems continue to rely on LLMs for both semantic interpretation and exact temporal inference. Consequently, discrete decisions regarding intervals, time anchors, and ordered states remain vulnerable to probabilistic errors and difficult to verify. We present STAIR, a \textbf{S}emantic-\textbf{T}emporal \textbf{A}utomaton for \textbf{I}nterpretable \textbf{R}easoning. STAIR separates semantic interpretation from precise temporal inference: an answer-free LLM adapter maps complex question formulations to normalized temporal intents, while a deterministic temporal automaton with finite control and guarded transitions executes the corresponding policies over canonicalized evidence. Following a rule-first design, STAIR resolves standard questions without invoking an LLM and applies semantic adaptation only when the rule path fails to produce an executable intent. This approach reduces free-form reasoning, making temporal decisions verifiable and interpretable. Specifically, guarded execution supports precise point-time containment and before/after selection, while semantic adaptation handles non-exact intervals and time-anchored queries. Across the TimeQA-Easy, TimeQA-Hard, TempReason-L2, and TempReason-L3 datasets, STAIR consistently outperforms strong baselines in the TQA task using matched model settings, achieving average F1 improvements of 16.57\% and 3.10\% when utilizing the Qwen2.5-7B and GPT-4o-mini models, respectively. Furthermore, ablations and diagnostic analyses demonstrate that STAIR excels at handling both boundary-sensitive and order-sensitive queries, while its guarded execution and semantic adaptation ensure precise point-time reasoning and inexact intervals, respectively.