Modality-Native Routing in Agent-to-Agent Networks: A Multimodal A2A Protocol Ex

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2605.11301v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have heterogeneous strengths across OCR, chart understanding, spatial reasoning, visual

arXiv:2605.11603v1 Announce Type: new Abstract: The growing deployment of large language models (LLMs) makes per-request routing essential for balancing response quality and

arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely

arXiv:2604.06696v1 Announce Type: new Abstract: The rapid development of AI agent systems is leading to an emerging Internet of Agents, where specialized agents operate across

arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator

arXiv:2608.28062v1 Announce Type: new Abstract: Multimodal search agents extend parametric knowledge with newly emerging and long-tail evidence from the open web. Yet many

arXiv:2603.24787v1 Announce Type: new Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine