Towards High-Level Semantic Intelligence

2026-07-27

AI Summary

This work addresses the lack of systematic research and unified frameworks in artificial intelligence for high-level semantic tasks such as humor, irony, metaphor, and empathy. It introduces, for the first time, the concept of High-Level Semantic Intelligence (HLSI), formally delineating its scope and proposing a comprehensive framework encompassing task taxonomies, data construction methodologies, modeling strategies, and evaluation protocols. By integrating advances in natural language processing, multimodal learning, and cognitive modeling, the study synthesizes existing datasets and algorithms to provide a thorough review of current research on high-level semantic understanding and generation across textual, spoken, visual, and multimodal contexts. This synthesis establishes a foundational theoretical basis and outlines key directions toward achieving human-like semantic intelligence.

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.

Problem

• High-Level Semantics
• Semantic Intelligence
• Artificial Intelligence
• Cognitive Reasoning
• Natural Language Understanding

Innovation

• High-Level Semantic Intelligence
• semantic complexity
• cognitive reasoning
• multimodal semantics
• AI survey