arXiv:2606. 14790v1 Announce Type: cross Abstract: LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited.
By Hanqi Li, Jing Peng, Zijian Wang, Lu Chen, Kai Yu
arXiv:2606. 05043v1 Announce Type: new Abstract: The last few years have witnessed major advances in the modeling and implementation of multiagent systems based on declarative interaction protocols.
By Samuel H. Christie V, Amit K. Chopra, Munindar P. Singh
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
By Benedikt Bollig, Matthias F\"ugger, Thomas Nowak
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
arXiv:2606. 09751v1 Announce Type: new Abstract: Foundation models are moving from response generation into operational roles.
By Arsalan Shahid, Gordon Suttie, Philip Black
The paper introduces control‑data flow separation to improve prompt optimization in multi‑agent large language model systems. By representing execution protocols as typed, validated program objects and keeping task‑relevant content as unstructured language, the method prevents prompt edits from corrupting critical routing, formatting, or termination signals. Experiments on synthetic reasoning, collaborative review generation, and insurance rating workflows show that this approach maintains 100% protocol validity while consistently enhancing task performance.
By Wentao Zhang, Syed Shariyar Murtaza, Junaid Ahmad Bhatti, Utkarsh Soni, Yifan Nie, Eugene Wen, Yuntian Deng
arXiv:2603. 24747v2 Announce Type: replace Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for formal verification of agent protocols.
By Andreas Schlapbach
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:2607. 17780v1 Announce Type: cross Abstract: ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions.
By Huiri Tan, Yikun Wang, Puyang Zhang, Shangyu Li, Jiasi Shen
arXiv:2607. 14138v1 Announce Type: cross Abstract: The most prevalent distributed systems today include people and their personal machines (smartphones).
By Ehud Shapiro
arXiv:2606. 05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language.
By Chen Huang, Yuhao Wu, Wenxuan Zhang
arXiv:2602. 06934v4 Announce Type: replace-cross Abstract: Grassroots Logic Programs (GLP) is a concurrent logic programming language in which logic variables are partitioned into paired readers and writers.
By Ehud Shapiro