STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering

2026-08-17

AI Summary

This study addresses the probabilistic errors and unverifiability inherent in neuro-symbolic systems for temporal question answering that rely on LLM reasoning. We propose STAIR, a framework that decouples semantic understanding from precise reasoning by employing an answer-free LLM adapter for intent mapping alongside deterministic temporal automata and a rule-prioritized guard execution mechanism to ensure verifiable and interpretable decision-making. Experimental results demonstrate that STAIR significantly enhances the handling of boundary- and order-sensitive queries, substantially outperforming baselines across four datasets. Specifically, the framework achieves average F1 score improvements of 16.57% with Qwen2.5-7B and 3.10% with GPT-4o-mini, validating its effectiveness in integrating neural semantics with symbolic temporal logic for robust temporal reasoning.

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.

Problem

• Temporal Question Answering
• Neuro-symbolic Systems
• Interpretable Reasoning
• Probabilistic Errors
• Verifiability

Innovation

• Semantic-Temporal Automaton
• Interpretable Reasoning
• Neuro-symbolic Separation
• Rule-first Design
• Guarded Execution