Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking

• URL: http://arxiv.org/abs/2502.16866v1
• Date: Mon, 24 Feb 2025 06:02:25 GMT
• Title: Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking
• Authors: Ruichen Zhang, Shunpu Tang, Yinqiu Liu, Dusit Niyato, Zehui Xiong, Sumei Sun, Shiwen Mao, Zhu Han,
• Abstract summary: Agentic AI has emerged as a key paradigm for intelligent communications and networking.<n>This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
• Score: 87.82985288731489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing complexity and scale of modern telecommunications networks demand intelligent automation to enhance efficiency, adaptability, and resilience. Agentic AI has emerged as a key paradigm for intelligent communications and networking, enabling AI-driven agents to perceive, reason, decide, and act within dynamic networking environments. However, effective decision-making in telecom applications, such as network planning, management, and resource allocation, requires integrating retrieval mechanisms that support multi-hop reasoning, historical cross-referencing, and compliance with evolving 3GPP standards. This article presents a forward-looking perspective on generative information retrieval-inspired intelligent communications and networking, emphasizing the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems. We first provide a comprehensive review of generative information retrieval strategies, including traditional retrieval, hybrid retrieval, semantic retrieval, knowledge-based retrieval, and agentic contextual retrieval. We then analyze their advantages, limitations, and suitability for various networking scenarios. Next, we present a survey about their applications in communications and networking. Additionally, we introduce an agentic contextual retrieval framework to enhance telecom-specific planning by integrating multi-source retrieval, structured reasoning, and self-reflective validation. Experimental results demonstrate that our framework significantly improves answer accuracy, explanation consistency, and retrieval efficiency compared to traditional and semantic retrieval methods. Finally, we outline future research directions.

Related papers

• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends [0.2999888908665658]
  The study highlights AI applications in adaptive signal processing, multi-agent coordination for network optimization, radar-assisted target tracking, and AI-driven electronic countermeasures. We address challenges such as adversarial AI threats, the real-time adaptability of autonomous communication networks, and the limitations of current AI models under battlefield conditions.
  arXiv  Detail & Related papers  (2025-04-07T13:38:32Z)
• Modular Conversational Agents for Surveys and Interviews [6.019313905775819]
  This paper introduces a modular approach and its resulting parameterized process for designing AI agents. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies. The results suggest that the AI agent increases completion rates and response quality.
  arXiv  Detail & Related papers  (2024-12-22T15:00:16Z)
• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers.<n>This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine [14.123823081267336]
  This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN) The ACN framework consists of multiple specialized agents working collaboratively, each with distinct roles such as Account Manager, Solution Strategist, Information Manager, and Content Creator. A highlight of the ACN is the introduction of a Reflective Forward Optimization method (RFO), which supports the online synergistic adjustment among agents.
  arXiv  Detail & Related papers  (2024-09-01T07:01:22Z)
• Learning Wireless Data Knowledge Graph for Green Intelligent Communications: Methodology and Experiments [36.82287751359216]
  We propose a pervasive multi-level (PML) native AI architecture, which integrates the concept of knowledge graph (KG) into the intelligent operational manipulations of mobile networks. We characterize the massive and complex data collected from wireless communication networks and analyze the relationships among various data fields. This architecture not only enhances AI training, inference, and validation processes but also significantly reduces resource wastage and overhead for communication networks.
  arXiv  Detail & Related papers  (2024-04-16T07:55:34Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning [14.929735103723573]
  We propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I) The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. We specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning.
  arXiv  Detail & Related papers  (2024-02-03T13:28:35Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• The Confluence of Networks, Games and Learning [26.435697087036218]
  Emerging network applications call for game-theoretic models and learning-based approaches to create distributed network intelligence. This paper articulates the confluence of networks, games and learning, which establishes a theoretical underpinning for understanding multi-agent decision-making over networks.
  arXiv  Detail & Related papers  (2021-05-17T20:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.