SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models

TL;DR

This paper introduces SemEval-2026 Task 12, a benchmark for abductive reasoning in real-world event causality, challenging systems to identify plausible causes from complex evidence, with extensive participation and promising insights for future causal reasoning research.

Contribution

It formulates a new evidence-grounded multiple-choice benchmark for abductive event reasoning, including dataset creation, evaluation setup, and analysis of system performance.

Findings

122 participants and 518 submissions

Highlights challenges in causal reasoning and multi-document understanding

Provides a benchmark for future research in event causality

Abstract

Understanding why real-world events occur is important for both natural language processing and practical decision-making, yet direct-cause inference remains underexplored in evidence-rich settings. To address this gap, we organized SemEval-2026 Task 12: Abductive Event Reasoning (AER).\footnote{The task data is available at https://github.com/sooo66/semeval2026-task12-dataset.git} The task asks systems to identify the most plausible direct cause of a target event from supporting evidence. We formulate AER as an evidence-grounded multiple-choice benchmark that captures key challenges of real-world causal reasoning, including distributed evidence, indirect background factors, and semantically related but non-causal distractors. The shared task attracted 122 participants and received 518 submissions. This paper presents the task formulation, dataset construction pipeline, evaluation setup, and system results. AER provides a focused benchmark for abductive reasoning over real-world events and highlights challenges for future work on causal reasoning and multi-document understanding.