{"ID":2861771,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00381","arxiv_id":"2510.00381","title":"Semantic-Driven AI Agent Communications: Challenges and Solutions","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.00381","url_pdf":"https://arxiv.org/pdf/2510.00381v1","authors":"[\"Kaiwen Yu\",\"Mengying Sun\",\"Zhijin Qin\",\"Xiaodong Xu\",\"Ping Yang\",\"Yue Xiao\",\"Gang Wu\"]","published":"2025-10-01T00:52:37Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"eess.SP\"]","methods":"[]","has_code":false}