TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

📰 ArXiv cs.AI

Learn to evaluate temporal causal reasoning in large language models using TempoBench and improve your understanding of their limitations

advanced Published 9 Jun 2026

Action Steps

Define temporal causal reasoning and its importance in large language models
Evaluate the performance of LLMs on temporal causal reasoning tasks using TempoBench
Identify the limitations of current LLMs in identifying minimal causal inputs
Apply the insights from TempoBench to improve the design and training of LLMs
Test and compare the performance of LLMs on temporal causal reasoning tasks using TempoBench

Who Needs to Know This

NLP researchers and developers can benefit from this knowledge to improve the performance of their language models, while AI engineers can apply these insights to develop more accurate and reliable AI systems

Key Insight

💡 TempoBench helps evaluate the ability of LLMs to identify minimal causal inputs, a key aspect of temporal causal reasoning

Key Takeaways

Learn to evaluate temporal causal reasoning in large language models using TempoBench and improve your understanding of their limitations

Full Article

Title: TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models

Abstract:
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 the minimal causal inputs of outcomes. To study this distinction, we define

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