Effective collaboration in multi-agent systems hinges on communication. Existing studies analyze agent communication indirectly through task performance, such as improvements in reward and success rate, without explicitly characterizing communication itself. As a result, the quality of communication protocols remains difficult to evaluate and interpret, limiting their reliability, scalability, and generalizability. To better characterize communication quality, this survey focuses on communication alignment, which emphasizes semantic fidelity and convention consistency across agents. We review communication alignment from emergent communication in multi-agent reinforcement learning (MARL) to latent communication powered by Large Language Models (LLMs), highlighting existing methods and the emerging challenges introduced by natural-language communication. Finally, we discuss future directions toward more effective, interpretable, and generalizable multi-agent communication.