The paper introduces the Natural Language Interaction Protocol (NLIP), a standards‑based application‑layer protocol designed to enable AI agents built on diverse frameworks and tools to communicate seamlessly. NLIP offers a lightweight semantic message envelope that can be transmitted over common transports such as HTTP/HTTPS, WebSocket, and AMQP, and includes mechanisms for adapting between clients, agents, local context stores, ontologies, tools, and enterprise services. The authors discuss the protocol’s design rationale, security considerations, reference implementation, representative applications, adoption signals, and its relationship to other emerging agent protocols like MCP and A2A.
By Luyi Xing, Rasit Onur Topaloglu, Ranjan Sinha, Abhay Ratnaparkhi, Samuel Ndichu, Christopher Nguyen, Anindita Das, Tom Sheffler, Mohamed Rahouti, Zichuan Li, Xiaojing Liao, Sanjay Aiyagari
arXiv:2606. 19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks.
By Linus Sander, Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll
The paper introduces a safety‑bounded gateway that translates IEEE 11073 Service‑Oriented Device Connectivity (SDC) into the Model Context Protocol (MCP) for medical AI agents. It exposes device metrics, alarms, context references, and semantic metadata as read‑only resources, while representing selected action affordances as policy‑validated dry‑run tools, ensuring that agent requests never trigger actual device operations. A Python prototype demonstrates fault and lifecycle experiments, deterministic baselines, and multi‑model agent evaluation, showing improved semantic conformity and preservation of the no‑execution boundary.
By Bennet Gerlach, Stefan Fischer
arXiv:2606. 01312v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems.
By Kiran Khurshid, Shumaila Javaid, Nasir Saeed
The paper introduces an architectural mediation approach that uses the Model Context Protocol (MCP) to bridge large language model (LLM) agents with data spaces. By implementing the Eunomia Agent, the mediation layer translates data space capabilities into structured, schema-driven tools that LLM agents can discover and invoke while respecting governance constraints. A prototype demonstrates end‑to‑end interaction across catalog discovery, metadata retrieval, and data service invocation without altering existing data space components, showing that protocol‑based mediation enables interoperable, standards‑aligned integration of AI agents into governed data‑sharing ecosystems.
By Jaime Alonso Ruiz, Carlos Aparicio, Gabriel Huecas, Joaqu\'in Salvach\'ua, Andres Munoz-Arcentales
arXiv:2606. 00991v1 Announce Type: new Abstract: Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations.
By Siyan Li, Zehao Wang, Jiachen Li, Kanok Boriboonsomsin, Matthew J. Barth, Guoyuan Wu
While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions. To address this limitation, this manuscript introduces an agentic MPC framework that enables context-aware, semantically adaptive control synthesis by integrating with large language model-based agents.
arXiv:2606. 12774v1 Announce Type: cross Abstract: While MPC effectively handles structured, diverse, and low-level specifications, it lacks the capability to dynamically incorporate high-level contextual information such as social norms, user intent, or natural language instructions.
By Yuya Miyaoka, Masaki Inoue
The paper introduces ADN‑Agent, an architecture that uses a large language model to orchestrate multiple domain‑specific models (DSMs) for active distribution network (ADN) management. It features adaptive intent recognition, task decomposition, and a unified communication interface for heterogeneous DSMs, along with a pipeline for fine‑tuning small language models on language‑intensive subtasks. Experiments show ADN‑Agent outperforms existing LLM application paradigms in coordinating DSMs for complex ADN operations.
By Xu Yang, Chenhui Lin, Haotian Liu, Qi Wang, Yue Yang, Wenchuan Wu
arXiv:2606. 30317v1 Announce Type: cross Abstract: The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services.
By Carson Rodrigues, Oysturn Vas
arXiv:2607. 06786v1 Announce Type: cross Abstract: Standards bodies, including TM Forum, 3GPP, and ETSI, are converging on Agentic AI as the foundation for next-generation network management, where Large AI Model (LAM)-based agents autonomously interpret intent, coordinate resources, and adapt operational behaviors at runtime.
By Petar Djukic, Sudipta Acharya, Takai Eddine Kennouche, Burak Kantarci
arXiv:2608. 05159v1 Announce Type: new Abstract: Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation.
By Xi Wang, Kun Li, Xianyao Ling, Gang Yin, Liang Zhang, Jiang Wu, Wenbo Lei, Jun Xu, Annie Wang, Fu Zhang, Weizhe Wang