A Toolset-First Paradigm Based on Large Language Model and Agent via Model Context Protocol for Intelligent Computation.

A Large-Language-Model-Oriented Dual-Channel Agent Framework for Intelligent Generation of Simulation Scenarios

Constructing simulation scenarios manually is time-consuming and often depends on platform-specific modeling experience. Existing large-language-model (LLM) methods are promising for interpreting operational documents, but they still struggle with long-document parsing, incomplete platform interfaces, auditable task execution, and cross-lingual equipment-name normalization. This paper proposes a dual-channel LLM-agent framework for intelligent generation of simulation scenarios. The method standardizes Word-based scenario inputs, extracts scenario elements into a schema-constrained JSON intermediate representation, and decomposes the generation process into planning and execution. A DLL/Lua dual-channel Plan Agent assigns basic object and scenario-property operations to a C# object model, while allocating fine-grained unit, mission, and environment operations to Lua-based scripting interfaces. An Exec Agent further integrates two-stage hot-pluggable tool loading, side-effect-aware read/write-separated scheduling, and a Smart-Matcher module that combines BM25 retrieval, multilingual vector retrieval, reciprocal rank fusion, and low-confidence LLM reranking. Experiments on representative red-blue simulation scenarios show an average end-to-end generation time of 168 s, a first-round planning success rate of 92.0%, a post-Replan success rate of 100.0%, and Top-1/Top-5 equipment matching performance of 95.6% and 98.4%, respectively.

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

From Formal Specifications to Executable Simulations: A Computation-Driven Metasystem for Agent-Based Modeling

A Systematic Survey of LLM-Based Agentic AI Frameworks for Multi-Agent Coordination and Interoperability

Large Language Models (LLMs) have spurred the development of agentic artificial intelligence systems that can reason autonomously, plan, use tools, integrate memory, and carry out multi-step tasks. Unlike conventional prompt-response systems, LLM-based agents extend generative models to goal-oriented architectures that can decompose complex objectives, interact with external environments, and coordinate actions in iterative workflows. The survey systematically reviews LLM-based agent frameworks, multi-agent collaboration architectures, internal communication mechanisms, emerging cross-system interoperability protocols, and open research challenges. The review uses a PRISMA-based methodology, including literature from 2020 to March 2026, with particular focus on prominent frameworks such as LangChain, AutoGPT, AutoGen, MetaGPT, CAMEL, ChatDev, and CrewAI. The survey discusses the transition from modular chain-oriented to stateful graph-oriented and autonomous execution models and presents a systematic taxonomy of multi-agent collaboration architectures, including hierarchical, peer-to-peer, and role-based models. It also reviews the main internal communication mechanisms used to facilitate coordination and information sharing between LLM-based agents and distinguishes them from emerging interoperability protocols designed to support interaction across heterogeneous agents, tools, services, and frameworks. The analysis indicates that the promise of LLM-based agents for scalable automation, collaborative reasoning, and complex workflow execution comes with significant challenges in long-horizon reliability, evaluation standardization, communication security, cost-efficient orchestration, governance, and the interpretability of emergent multi-agent behavior. The survey covers architectural evolution, collaboration patterns, communication mechanisms, framework-level characteristics, and open research challenges. This provides a structured foundation for future research on reliable and trustworthy agentic artificial intelligence systems.

AgentWeave: Routing Before Reasoning for Efficient Function Calling in Tool-Rich Language Models

AgentWeave is introduced, a deterministic pre-inference routing layer that constructs a bounded model-visible action space using eligibility, requirement, capability, and routing signals and shows lower mean local-model latency.

Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog

Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains. Recent neuro-symbolic approaches address this gap by coupling neural models with external symbolic engines, yet most integrations are bespoke and lack a standardized interface for tool-augmented agents. This paper presents Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. Euclid-MCP introduces Euclid-IR, an engine-agnostic intermediate representation for Horn-clause logic that is human-readable, easy for LLMs to generate, and straightforward to compile into Prolog or alternative backends. The server exposes a compact tool interface that supports a translate-run-inspect-repair loop, enabling LLM clients to delegate inference while retaining full access to proof traces and derivation logs. We evaluate Euclid-MCP on a realistic IT security and compliance use case. Results show that while LLMs alone are sufficient on small knowledge bases, they hallucinate systematically on larger problems, whereas Euclid-MCP delivers exact answers with lower latency and more compact outputs. We argue that semantic RAG is fundamentally unsuited for rule enforcement, and that Euclid-MCP can serve as a stable, shared reasoning substrate for both RAG-based assistants and agentic systems.