Towards High-Level Semantic Intelligence
2026-07-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain that AI has evolved from handling simple, direct meanings to understanding and creating more complex ideas, which they call High-Level Semantics (HLS). They compare this shift in AI to how humans develop deeper understanding over time and name it the move from Basic-Level Semantic Intelligence to High-Level Semantic Intelligence. The paper reviews research on complex language tasks like humor, sarcasm, and empathy across different types of data such as text and images. The authors also summarize how these tasks are studied, modeled, and evaluated to help AI become more human-like in understanding and generating meaning.
Semantic ComplexityHigh-Level SemanticsBasic-Level Semantic IntelligenceHigh-Level Semantic IntelligenceHumor RecognitionMetaphor UnderstandingMultimodal AICognitive ReasoningAI Evaluation Methods
Authors
Xiujie Song, Gefei Yang, Yining You, Jiahui Gan, Qi Jia, Shota Watanabe, Tianxi Wan, Mengyue Wu, Kai Yu
Abstract
Recent advances in AI have substantially expanded its cognitive and reasoning capabilities. From the perspective of semantic complexity, the development of AI reveals a clear trajectory from simple to complex semantic processing. While early AI systems mainly addressed tasks involving direct and literal semantic perception or expression, contemporary systems are increasingly expected to perform more sophisticated cognitive reasoning, enabling the understanding and generation of High-Level Semantics (HLS). A similar trajectory can also be observed in human cognitive development. We define this transition as the shift from Basic-Level Semantic Intelligence (BLSI) to High-Level Semantic Intelligence (HLSI). However, this issue has not yet been systematically and comprehensively examined in prior work. Motivated by this gap, this survey reviews the development of AI semantic intelligence from the perspective of semantic complexity. We systematically survey existing research on HLS tasks, including humor, sarcasm, metaphor, empathy, persuasion, narrative, and other general HLS phenomena, across text, speech, vision, and multimodal scenarios. Specifically, we summarize data construction methods, modeling and optimization strategies, and evaluation methodologies for both understanding and generation. HLS is essential for advancing AI toward genuinely human-like intelligence. By synthesizing existing methods and insights from the perspective of semantic intelligence, this survey aims to support the continued development of AI toward HLSI.