Semantic-Driven AI Agent Communications: Challenges and Solutions

• URL: http://arxiv.org/abs/2510.00381v1
• Date: Wed, 01 Oct 2025 00:52:37 GMT
• Title: Semantic-Driven AI Agent Communications: Challenges and Solutions
• Authors: Kaiwen Yu, Mengying Sun, Zhijin Qin, Xiaodong Xu, Ping Yang, Yue Xiao, Gang Wu,
• Abstract summary: This article proposes a semantic-driven AI agent communication framework and develops three enabling techniques.<n>First, semantic adaptation transmission applies fine-tuning with real or generative samples to efficiently adapt models to varying environments.<n>Second, semantic lightweight transmission incorporates pruning, quantization, and perception-aware sampling to reduce model complexity and alleviate computational burden on edge agents.<n>Third, semantic self-evolution control employs distributed hierarchical decision-making to optimize multi-dimensional resources, enabling robust multi-agent collaboration in dynamic environments.
• Score: 25.74271088658268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid growth of intelligent services, communication targets are shifting from humans to artificial intelligent (AI) agents, which require new paradigms to enable real-time perception, decision-making, and collaboration. Semantic communication, which conveys task-relevant meaning rather than raw data, offers a promising solution. However, its practical deployment remains constrained by dynamic environments and limited resources. To address these issues, this article proposes a semantic-driven AI agent communication framework and develops three enabling techniques. First, semantic adaptation transmission applies fine-tuning with real or generative samples to efficiently adapt models to varying environments. Second, semantic lightweight transmission incorporates pruning, quantization, and perception-aware sampling to reduce model complexity and alleviate computational burden on edge agents. Third, semantic self-evolution control employs distributed hierarchical decision-making to optimize multi-dimensional resources, enabling robust multi-agent collaboration in dynamic environments. Simulation results show that the proposed solutions achieve faster convergence and stronger robustness, while the proposed distributed hierarchical optimization method significantly outperforms conventional decision-making schemes, highlighting its potential for AI agent communication networks.

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