来源: https://arxiv.org/abs/2504.19678
[2504.19678] From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
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Computer Science > Artificial Intelligence
arXiv:2504.19678 (cs)[Submitted on 28 Apr 2025 (v1), last revised 6 Mar 2026 (this version, v2)] Title:From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review Authors:Mohamed Amine Ferrag, Norbert Tihanyi, Merouane Debbah View a PDF of the paper titled From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review, by Mohamed Amine Ferrag and 2 other authors View PDF
Abstract:Large language models and autonomous AI agents have evolved rapidly, resulting in a diverse array of evaluation benchmarks, frameworks, and collaboration protocols. Driven by the growing need for standardized evaluation and integration, we systematically consolidate these fragmented efforts into a unified framework. However, the landscape remains fragmented and lacks a unified taxonomy or comprehensive survey. Therefore, we present a side-by-side comparison of benchmarks developed between 2019 and 2025 that evaluate these models and agents across multiple domains. In addition, we propose a taxonomy of approximately 60 benchmarks that cover general and academic knowledge reasoning, mathematical problem-solving, code generation and software engineering, factual grounding and retrieval, domain-specific evaluations, multimodal and embodied tasks, task orchestration, and interactive assessments. Furthermore, we review AI-agent frameworks introduced between 2023 and 2025 that integrate large language models with modular toolkits to enable autonomous decision-making and multi-step reasoning. Moreover, we present real-world applications of autonomous AI agents in materials science, biomedical research, academic ideation, software engineering, synthetic data generation, chemical reasoning, mathematical problem-solving, geographic information systems, multimedia, healthcare, and finance. We then survey key agent-to-agent collaboration protocols, namely the Agent Communication Protocol (ACP), the Model Context Protocol (MCP), and the Agent-to-Agent Protocol (A2A). Finally, we discuss recommendations for future research, focusing on advanced reasoning strategies, failure modes in multi-agent LLM systems, automated scientific discovery, dynamic tool integration via reinforcement learning, integrated search capabilities, and security vulnerabilities in agent protocols.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2504.19678[cs.AI]
(or arXiv:2504.19678v2[cs.AI] for this version)
https://doi.org/10.48550/arXiv.2504.19678
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arXiv-issued DOI via DataCite
Submission history From: Mohamed Amine Ferrag[view email][v1] Mon, 28 Apr 2025 11:08:22 UTC (11,920 KB)[v2] Fri, 6 Mar 2026 19:01:27 UTC (6,020 KB)
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