arXiv AI

Langshaw: Declarative Interaction Protocols Based on Sayso and Conflict

arXiv:2606. 29601v1 Announce Type: cross Abstract: Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings.

arXiv AI
Sep 2

Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs

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 AI
Sep 4

The Natural Language Interaction Protocol and Standard for AI Agents

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 AI
Jul 21

ETAS: An Effect-Typed Language for Agent Systems

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