# GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer
and Reasoning

Hang Zou, Qiyang Zhao, Lina Bariah, Yu Tian, Mehdi Bennis, Samson Lasaulce,
Mérouane Debbah, and Faouzi Bader

###### Abstract

GenAI and communication networks are expected to have groundbreaking synergies
in 6G. Connecting generative artificial intelligence (GenAI) agents over a
wireless network can potentially unleash the power of collective intelligence
and pave the way for artificial general intelligence (AGI). However, current
wireless networks are designed as a “data pipe” and are not suited to
accommodate and leverage the power of GenAI. In this paper, we propose the
GenAINet framework in which distributed GenAI agents communicate knowledge
(high-level concepts or abstracts) to accomplish arbitrary tasks. We first
provide a network architecture integrating GenAI capabilities to manage both
network protocols and applications. Building on this, we investigate effective
communication and reasoning problems by proposing a semantic-native GenAINet.
Specifically, GenAI agents extract semantic concepts from multi-modal raw
data, build a knowledgebase representing their semantic relations, which is
retrieved by GenAI models for planning and reasoning. Under this paradigm, an
agent can learn fast from other agents’ experience for making better decisions
with efficient communications. Furthermore, we conduct two case studies where
in wireless device query, we show that extracting and transferring knowledge
can improve query accuracy with reduced communication; and in wireless power
control, we show that distributed agents can improve decisions via
collaborative reasoning. Finally, we address that developing a hierarchical
semantic level Telecom world model is a key path towards a network of
collective intelligence.

##  I Introduction

The sixth generation (6G) wireless network is envisioned to be AI-native, in
the sense that wireless communications will be an integrated part of training
and inference. Conventionally, wireless networks are designed for data
collection and transmission, aiming at achieving a targeted quality of service
(QoS). However, they are not designed to support a massive deployment of AI-
empowered devices, especially with large AI models which have high
communication and computing costs. LLM built on generative pretrained
transformers (GPT) have shown impressive capabilities, from question answering
and language understanding, to mathematical and common sense reasoning [1].
Such capabilities facilitate the wide adoption of large language models in
robotics, Telecom, finance, healthcare, and so on. The maturity of LLMs, which
is achieved by training on massive data and compute, constitutes an important
step towards artificial general intelligence (AGI) which signifies human-level
intelligence. However, the vast majority of existing LLMs are deployed in the
cloud, limiting their applicability on devices due to constraints pertinent to
bandwidth, latency, and security. Connecting distributed LLMs through wireless
networks paves the way to enable multi-agent collective intelligence [2].
Therefore, two major challenges appear: how to efficiently embed LLMs to
wireless devices and how to connect them with low cost.

To deploy LLMs on resource limited devices, various techniques have been
proposed, such as model compression, inference acceleration, and parameter-
efficient fine tuning. For instance, the T5-770M model has been shown to
outperform the PaLM-540B in some tasks [3]. Moreover, paged attention proposed
in vLLM can reduce the average inference time of LLMs down to 1/24 compared to
vanilla methods [4]. Furthermore, QLoRA [5] is an efficient fine-tuning
technique which can quantize an LLM to a 4-bit version with a small set of
additional learnable low-rank adapters tuned using quantized weights. Although
efficient in model size and inference time reductions, such techniques can
degrade LLM’s performance, e.g., in terms of generalization capability, due to
the non-modular nature of LLMs.

To use LLMs in complex tasks solving, a multi-agent framework is required. An
agent can observe the environment, plan a sequence of actions, create high-
level reflections of experience in a memory stream, and formulate plans. For
example, BabyAGI is a task-driven autonomous agent framework that can
generate, execute and prioritize tasks in real-time. Auto-GPT can chain
together LLM “thoughts” in an infinite loop of reasoning [6]. On the other
hand, multi-agent systems are shown to largely enhance LLMs’ capability in
task solving [7]. For example, CAMEL explores two role-playing communicative
agents with autonomous cooperation [8]. In generative agents, believable human
behaviors are simulated in a sandbox environment, where multiple agents
interact in natural language to complete spontaneous tasks [9]. However,
communication, computing, and storage efficiency are not considered in these
frameworks, which are critical factors in wireless networks.

Motivated by the reasons above, in this paper, we propose the GenAINet
framework in which multiple GenAI agents communicate knowledge to perform
effective reasoning and solve arbitrary tasks of particular applications,
e.g., networks of vehicles, smart grid, and internet of things. In GenAINet,
agents utilize LLMs to learn a high-level concept or abstract of data, plan
reasoning path, and execute it to achieve a goal. The agents communicate
knowledge to help each other’s planning and decision. In doing so, the
performance can be improved with less communication and computing costs. The
main contributions of this article are as follows:

  * •

A GenAINet architecture is discussed and possible approaches to integrate LLMs
are proposed. We propose a unified agent architecture to manage both network
protocols and applications;

  * •

A communication paradigm for GenAINet, built on semantic concepts and multi-
path reasoning, is proposed. With semantic concepts, multi-modal raw data can
be embedded on a common semantic space. With multi-step planning, we explain
how an LLM can execute and optimize reasoning paths towards effective
decisions. Semantic concepts learned from past reasoning and decision can be
sent to or retrieved by remote agents, to improve independent or collaborative
planning;

  * •

We investigate two case studies of wireless GenAINets. In the first case of
question answering on a mobile device, we show transferring knowledge from a
teacher to a student agent can improve query accuracy and communication
efficiency; in the second case of wireless power control, we show that
distributed GenAI agents can collaborate with reasoning to find a power
allocation solution for a targeted data rate;

  * •

We discuss challenges and explore future research directions on GenAINet,
including building a hierarchical world model with multi-level abstractions
and planning to ground the model in real network scenario.

##  II GenAI Network and Agent Architectures

Wireless GenAI agents can emulate human-like decision making process,
providing a path towards sophisticated and adaptive GenAI networks. It can
bring autonomy to network protocols and network applications. To achieve this,
we propose prospect architectures for GenAI networks and GenAI agents, as
illustrated in Fig. 1.

Figure 1: Proposed wireless GenAI network and agent architecture with protocol
and application management.

###  II-A Wireless GenAI Network Architecture

In a wireless network, a GenAI agent can potentially act as an autonomous
controller in the following scenarios.

Network protocols: GenAI agents can orchestrate network resources and control
network functions in radio access, core, and transport domains. It can
interface with network protocols to control power, resource, traffic, etc. The
GenAI network protocol architecture can include:

  * •

Hierarchical architecture: GenAI agents can be deployed in multi-level network
controllers, sending policies to the data network. For instance, a commander
agent operates in the RAN intelligent controller (RIC), while multiple
executor agents operate in the network elements (NE). The commander
deconstruct intents into subtasks and assigns to the executors.

  * •

Distributed architecture: GenAI agents can be co-located with distributed NEs
and manageme locally the data and control plans. The interaction between
agents is self-organized, where agents exchange past decisions or knowledge to
collaboratively accomplish the tasks.

In autonomous networks, GenAI agents can break down high-level intents, plan
actionable tasks, generate network control strategies, and refine them from
feedback.

Network applications: GenAI agents can bring autonomy to network applications,
such as autonomous vehicles and robots. Wireless networks allow GenAI agents
to collaboratively perform various tasks, such as remote sensing, control, and
planning. The GenAI network application architecture can include:

  * •

Independent architecture: Each application is controlled by individual GenAI
agents. For example, in a vehicular network, the network agents control base
stations, user equipments (UEs) and the traffic agents control cars, traffic
lights.

  * •

Converged architecture: GenAI agents can jointly control network protocols and
applications. For example, a car agent can jointly control engine, steering
and communication protocols with other cars, such that multiple cars can
collaboratively improve the traffic flow.

The hierarchical and independent architecture can be built over existing 5G
wireless networks where applications are over-the-top services. On the other
hand, the distributed and converged architectures are more disruptive for 6G,
fully implementing the protocol and application convergence with multi-agent
network.

###  II-B Wireless GenAI Agent Architecture

To enable LLMs to interact with real-world scenarios, an agent has to be of a
distributed structure, decision-wise and information-wise. An autonomous agent
generally comprises four components: perception, action, planning, and memory.
In what follows, we explain how these components can be designed to implement
the proposed vision.

Perception is the part of an agent gathering useful and relevant information
to an LLM. Possible information sources for perception include: 1) information
sensed from the environment, e.g., channel state information (CSI), locations
of user equipment (UE) and BS in a network, or on an autonomous car, the
traffic density, speed and visual data of streets; 2) information received
from other agents, such as messages reporting their state or responses to some
requested information, or strategies learned from past decisions under
different states. The information can be represented by using different
modalities, from raw text, images, to abstracted graphs. The perceptor should
fuse and encode raw data in a common embedding space, for the LLM to perform
further decision-making.

Action is the component managing all available tools of an agent. Actions
generated by the LLM should be adapted to the target functions or interfaces
to execute. For a wireless GenAI agent, possible actions may include: 1)
information generated to complete a task, such as the responses to a query
from user, or messages sent to another agent to complete a task; 2) executing
predefined interfaces and toolkits, such as the function call in a MAC
scheduler, configurations in the radio amplifier, or commands to adjust the
car speed and steering. Furthermore, the action in a multi-agent network
includes actions taken by observing the environment and communication actions
(messages) to be sent to other agents. Both should be generated from a learned
joint strategy.

Planning is the process of creating and optimizing actions over time to
achieve a goal including: 1) sub-task decomposition, i.e., breaking down a
high-level goal into sub-goals and actionable tasks; 2) synchronize and
prioritize existing tasks between agents; 3) self-reflection, which evaluates
and criticizes the past decisions to optimize the policy. Planning allows LLMs
to solve complex tasks via multi-step reasoning, such as chain-of-thoughts
(CoTs). Furthermore, such planning and reasoning can be conducted
collaboratively among multiple agents by leveraging computing resources and
tools on different devices efficiently.

Memory is the module for storing short-term experience and long-term knowledge
for LLMs’ future planning and decisions. Short-term experience is the selected
history of observations, actions, thoughts, and conclusions of agents. It is
necessary for agents to efficiently reuse or adapt existing solutions or
mechanisms instead of reasoning and extracting from scratch. Moreover, the
long-term knowledge contains facts, methods, and contents which could be
general or domain-specific depending on the role of an agent. Having such
knowledge could effectively reduce the chance of an LLM to produce non-
reliable or non-factual responses by using retrieval-augmented techniques.
Possible knowledge representations and how LLMs leverage them to enhance the
quality of inference will be covered in next section. Empowered by LLMs’
knowledge, GenAI agents can release the need for environment modeling or
problem formulation, while finding the reasoning path towards task completion.
This makes GenAI effective in handling complex, unseen problems.

##  III Wireless GenAINet with Knowledge driven Communication and Reasoning

Existing LLMs and GenAI agents operate on raw data. For example, agents
memorize and exchange information in natural language. Communication between
AI models on the network edge is typically done by exchanging model weights,
gradients, or hyper-parameters. However, such design schemes become
inefficient when massive GenAI agents are connected. First, raw data contains
a large amount of redundant information which is inefficient for
communication; Second, optimizing LLMs over networks for different tasks is
energy-consuming and may induce large latency. To address the aforementioned
issues, we propose a semantic-native GenAINet. Empowered by LLM’s abstraction
and planning capabilities, the agents communicate semantic concepts or
abstracts learned from reasoning and decision making. In doing so, agents can
learn to communicate or collaborate to achieve a goal, with reduced
communication, computing and storage costs.

###  III-A Semantic knowledge representation

LLMs have been trained to understand semantics. Extracting knowledge from raw
data to a concept space allows LLMs to effectively compose information for
different tasks, which leads to better generalization. Knowledge includes
information abstracted from a wide range of experiences. In GenAINet, this can
be retrieved from common sources such as the web. Besides knowledge can be
learned from an agent’s plan-and-execute experiences. For example, a car agent
may learn to decelerate when observing other cars merging from the side lane.
Another example, is when a solver agent may share with other solver agents an
intermediate result about its calculation or computation process. Both
retrieved and learned knowledge are thus vital for effective decision-making
or generation.

Wireless GenAINet being constrained by communication, computing, and storage
resources, knowledge transferred and stored in the network should satisfy
minimality and sufficiency, that a minimal amount of information can solve a
wide range of tasks or problems effectively. To realize this, we can decompose
knowledge into two components: a) common knowledge in a domain which can be
injected into LLMs via fine-tuning; b) evolving knowledge operating on top of
common knowledge for specific tasks, which should be transferred in the
network and used by LLMs. In GenAINet, managing common and evolving knowledge
properly is the key to achieve high efficiency. In what follows, three
possible representations for knowledge which are relevant for communication
between LLMs are discussed.

VE is a commonly used knowledge representation for LLMs. It embeds raw data
(text, image, audio) with latent vectors to construct a database (DB), where
the distance between vectors represents their semantic similarities. When an
LLM receives a user prompt, it first leverages time-efficient methods, e.g.,
approximate nearest neighbors, similarity search to locate relevant clusters
based on similarity measure. A precise local research will be applied to
extract related information, which is concatenated with the user prompt for
LLMs to generate responses. RAG provide additional context to improve LLMs’s
performance in a time varying domain.

KG is a structured representation of relations between real objects or
abstracted concepts. These relations can describe connections and causalities
between entities in natural language, making it easily accessible for an LLM.
Two vertices would be connected by an edge if they are correlated and the
weight of the edge may indicate the degree of correlation. knowledge graph
(KG) can assist an LLM to perform fast information retrieval, and efficient
reasoning based on the structure of entities. Moreover, KG can also help LLMs
to understand and generate more contextually relevant and coherent responses
for complex tasks requiring factual responses.

TE: Topological models, e.g., hypergraphs, simplicial complexes and cell
complexes, can represent the intrinsic structure of data. They can model the
low-order (vertices) and high-order (simplices, cells) relations between
syntactic objects or abstracted concepts. Unlike conventional neural networks
learning data structure on 1D sequence or 2D grid, topological embedding (TE)
can exploit more flexible and dynamic structure. Moreover, the high-order
topological structure can represent more complex and causal relations, such as
different word combinations by semantic features. Therefore, TE can exploit
more implicit latent structures than KG. The geometric features allows for
flexible structure composition according to the changes of semantic features,
which saves communication and computing resources.

###  III-B Multi-modal semantic reasoning

Semantic knowledge allows GenAI agents to compress, transfer, and retrieve new
information. However, conventional structures built on the observation space
lack generalization in new scenarios and domains. Therefore, building
structures on a semantic concept space is essential. This requires aligning
multi-modality raw data on a common embedding space and extracting semantic
concepts. Techniques such as contrastive learning and multi-modal cross-
attention (e.g., ImageBind [10]) have been used to align multi-modal data,
extendable to RF signals as well. After training cross-modality encoders,
semantic concepts can be extracted from raw data uniformly.

Semantic knoweldgebase can be built by learning a topological structure
connecting semantic concepts. The first step is to exploit hidden semantic
concepts from observations. Connections between semantic concepts can be
learned from their semantic relations and causalities, which can be
represented by different topological structures (e.g., edges and cliques).
With logical combination of explicit concepts we can discover hidden concepts.
This process can learn all possible semantic structures from raw data, then
serving as a “world model” in the cloud. Subsequently, minimal and sufficient
semantic concepts can be retrieved from semantic knoweldgebase to serve
specific tasks, e.g., by clustering the concepts according to the semantic
information required for a task.

The semantic concepts are then used on agents to perform planning and
reasoning, for making decisions or generating contents. CoT prompting could be
used to decomposes a problem into coherent sequences serving as intermediate
steps, with an interpretable window suggesting how it might reach the final
solution, and gives the agent opportunity to debug when a step goes wrong
[11]. CoT could be generalized to sequential or non-sequential complex
structures such as tree or graph of thoughts, with rewards to optimize the
path. Furthermore, the decomposed sub-tasks of a complex task can be assigned
to different agents and be solved separately. Finally the outputs from each
sub-task can guide agents to complete the task jointly.

Fig. 2 illustrates the process of multi-modal semantic reasoning. Semantic
concepts $c$ are learned from multi-modal raw data. A semantic knowledgebase
(KB) representing the semantic relations between concepts on topological
structure is built. When an agent receives an input, it plans a tree of
thought states $T$. In each state it retrieves concepts from semantic KB and
produce an action. The final output is generated after completing the planned
states. The agent can observe rewards at each state, optimize the reasoning
path and the semantic concept structures in the KB.

Figure 2: Proposed pipeline of multi-modal semantic extraction, retrieval and
reasoning on GenAINet agents.

###  III-C Semantic-native GenAINet

With semantic knowledge and semantic reasoning, GenAI agents can collectively
learn communication protocols and decision strategies for solving different
tasks, leading to an efficient GenAINet. GenAI agents can be used for various
tasks on connected devices, from generating content to making decisions. An
effective communication protocol should be part of the reasoning process,
guiding agents to achieve a goal, such as the accuracy of responses, the
accumulated reward of actions, and so on. Furthermore, an agent should
effectively manage the knowledge to transmit, memorize, and update its model
according to the information freshness and energy cost.

GenAINet can be implemented in a teacher-student or a distributed paradigm.
The teacher-student paradigm is applicable for communication between cloud,
edge, and device agents, where different sizes of LLMs are deployed.
Specifically, a teacher agent with LLM trained on universal knowledge guides a
student agent to perform specific tasks via knowledge transfer. To reduce
communication cost, the teacher agent extracts knowledge in specific domains
and transfer to the student, which is efficient for devices performing
specific tasks (e.g., routing, traffic control). The distributed paradigm can
be used for communication between mobile devices, machines, vehicles, where
agents have similar capabilities and need to collaboratively complete tasks.
Each agent has a memory of emergent knowledge which is learned from its past
experience. During planning, the agent can retrieve or communicate knowledge
with others to optimize decisions and update local knowledge. In a long term,
the agents will share a common KB and make decisions locally, where
communication costs can be minimized until observing new scenarios or tasks.
Compared to standard communication systems, GenAINet is resource efficient and
task effective. For example, in a remote query scenario the teacher-student
paradigm can reduce load and latency in sending responses; in an autonomous
vehicle (AV) scenario the distributed paradigm can improve driving safety and
communication reliability.

Fig. 3 illustrates an example of GenAINet communication between AVs. The car
agent utilizes LLMs as knowledge retriever (sender) and decision maker
(actor). Communication is part of knowledge retrieval or sharing with a remote
agent, which is planned according to actions and observations in a reasoning
path. This makes the information exchange efficient and effective for decision
making. The retriever aggregates information from local and remote KB, pass it
to the actor to generate driving commands. The actor optimizes reasoning path
from environment feedback. For example, it can reward the CoT to find the best
path and action. Once complete, the agent extracts knowledge (e.g., learned
rules) from the recent planning and store it in the memory.

We shown here three typical use cases of semantic communication between car
agents. The first case is semantic compression from multi-modal sensory data.
LLMs can produce semantic latent on source textual data for lossless
generation [12]. With multi-modal LLMs, semantic compression can be used to
gather information efficiently from remote sensor with different field-of-
views or resolutions, to improve downstream tasks such as accident detection,
navigation. The second case is collaobrative knowledge, where agents exchange
the abstracts and concepts from memory. This can be achieved by remote
retrieval, where the agent performs semantic similarity search on the request
from remote agent, and send the related information. The third case is
semantic reasoning, where agents find the consequence of past actions and
exchange with others. This can reduce agents’ reasoning latency and improve
decision reliability. Since long-term knowledge is more generalized than
short-term experience, the communication cost can be reduced.

Figure 3: Three use case of semantic native GenAINet: compression, control,
and reasoning.

##  IV Case Studies of GenAINet

In this section, we show how the GenAINet can be applied to two examples
namely, wireless device query and wireless power control. Specifically, we
want to show: 1) How GenAI agents can efficiently transfer knowledge to
perform on-device query; 2) How GenAI agents can collaborate with reasoning to
solve a wireless network problem.

###  IV-A Knowledge transfer for wireless device query

Question answering (QnA) is a typical application of LLMs. Deploying LLMs
close to the end user can largely reduce the latency and traffic burden from
massive connections. Despite that light-weight LLMs are built with efficient
inference techniques, they exhibit poor performance in specialized domains
compared to regular LLMs. RAGs can enhance LLM’s knowledge with external
database while introducing storage cost and higher latency. Envisioned from
GenAINet, we propose a semantic knowledge driven on-device query. As shown in
Fig. 4, In the cloud agent, its LLM extracts the related context for the
queries from cloud data and compress them into latent representations to build
a semantic KB. It is then sent to the LLM on a device to answer questions
using RAG. The device can feedback questions to improve semantic extraction.

We have performed experiments on the TeleQnA [13], including 10k Telecom
domain QnA from research and standard materials. We extract the questions’
sources and build a large vector DB in the cloud. An initial batch of sampled
questions are fed into GPT-3.5 to perform retrieval from the DB, under
instruction prompts to generate contexts related to the questions, and build
the semantic KB for RAG on device. Table I shows that the semantic KB
significantly improves the TeleQnA accuracy in all categories compared to the
base Llama-7B, by reducing 27% exchanged information compared to cloud based
QnA. The use case shows that an LLM can extract semantic knowledge and assist
other LLMs to complete a task which is more efficient than sending raw data.

Figure 4: Cloud LLMs extract semantic knowledge from raw text and send to LLM on devices for QnA with RAG. QnA category |  GPT-3.5-turbo accuracy |  Semantic RAG accuracy |  Reduced bits exchange
---|---|---|---
Lexicon | 96 | 100 | 31.09
Research overview | 66.35 | 72.12 | 26.61
Research publication | 66.98 | 83.72 | 27.43
Standard overview | 64.52 | 85.48 | 26.59
Standard specification | 56.38 | 71.28 | 23.91
Overall | 66 | 80 | 27.13
TABLE I: QnA accuracy and reduced bits exchange (%), compared to exchanging
raw QnA data.

###  IV-B Collaborative reasoning for wireless power control

Conventional power control paradigm resorts to an optimization problem by
modeling the environment and finding the best power level leading to assigned
performance metric (rate, energy). With the proposed paradigm, the solving is
performed by exploiting LLMs’ knowledge of wireless network (acquired during
pre-training), to reason over the path from observed rate to best power. This
means in particular that with GenAI agents we may release online training.

We consider a scenario of 20 paired Tx-Rx users randomly placed in a
$100m^{2}$ area. Users share a common spectrum where interference is
encountered. Each user pair is associated with an LLM agent, including an LLM
with plan-and-solve prompting, plus a memory of past data rate observations
and power allocation actions. We aim to reduce the data rate gaps towards a
target so as to minimize the total power in the network. The radio environment
information is unknown to the agents and we instruct LLMs to exploit its
memory and its telecom knowledge. In each iteration each LLM should decide its
power and explain its decision.

The mean absolute error of data rate of all users towards the target is shown
in Fig. 5. We evaluated 3 scenarios: 1) standalone agents without
interactions; 2) agents share memory of past observations and actions; 3)
agents share an explanation of the past decisions. It can be seen that the
standalone agents find the power optimization path towards the targeted rate
after several rounds. With memory sharing, agents learn slowly to understand
the propagation and interference environment and adjust their power in the
right direction. Finally, the explanation from other agents in previous rounds
can help LLMs to find better power allocation scheme quickly. The example
shows that sharing reasoning outputs can effectively help LLMs to improve
decision making without online training.

Figure 5: Performances of data rate gap towards target with example prompts to
LLM agents on power control.

##  V Challenges and Opportunities

We envision GenAINet as a vital part for enabling collective intelligence in
6G. However, there are several challenges which opens up new research
opportunities.

LLMs are auto-regressive generative models pre-trained on natural text to
predict the next token. Therefore LLMs hardly predict high-level semantics,
which introduces high information redundancy and computation cost in training
and inference. Besides, LLMs’ generalization capability is usually limited in
domains not included in the training data. RAG and fine-tuning are commonly
used to enhance LLMs’ knowledge while being expensive and inflexible.
Furthermore, grounding LLMs’ knowledge to real world representation is
challenging. Reinforcement learning is studied to optimize LLM online, which
is not suitable for computational limited devices.

World model is a hierarchical, modular model predicting future representations
of the state of the world [14]. It is trained to predict the high-level
abstract instead of raw data. For example, I-JEPA [15] is trained to predict
missing embedding on an image, which shows less training data and
computational effort. Furthermore, hierarchical JEPA (H-JEPA) allows to learn
higher level abstract representation which is effective for long-term
prediction and eliminate the irrelevant details. Built on H-JEPA, hierarchical
planning can be done by predicting state transition on abstract space, which
could handle uncertain environment with minimal cost. JEPA framework gives
promises for deploying world model on wireless devices than LLMs. Since RF
propagation is more abstract than text, training RF-JEPA could be more
efficient than RF-GPT. However, JEPA on multi-modality and planning still
require further research.

While GenAI agents are used in various domains, applying them in wireless
networks is still challenging. First, a network has a complex hierarchical
architecture from RF to service layers, making LLMs difficult to decompose
tasks for every network element. Second, orchestrating numerous agents’
behavior is difficult for networks deployed in large scale geographical areas.
Therefore, current LLMs are used only in small domains, mostly service layer.
Moreover, future networks require much higher reliability and robustness,
where the uncertainty of GenAI models is problematic. Finally embedding RF
signals into LLMs is challenging, primarily due to the unavailability of
appropriate large datasets and the inherent nature of RF signals, which is
both spectral and spatial. This fundamentally differs from the textual data.

##  VI Conclusion

In this paper, we introduced the GenAINet, a knowledge driven communication
and reasoning network as a promising enabler for collective intelligence. The
architecture of GenAINet leverages unified LLM-powered GenAI agents to
optimize both network protocols and applications. Our semantic-native
framework can learn structure of semantic concept or abstract from multi-modal
raw data, and build semantic knowledgebase to achieve effective communication
and reasoning. Agents utilize LLMs with minimal semantic concepts retrieved
locally and remotely through semantic communications to effectively plan
reasoning paths and produce effective decisions for a task. We investigated a
use case of transferring semantic knowledge between teacher and student agents
to improve on-device query with less communication costs, as well as an
example of distributed agents communicate with reasoning to improve wireless
power control. We demonstrated that our multi-agent GenAINet can unleash the
power of collective intelligence, and addressed that developing a semantic-
native Telecom world model is an essential path towards network collective
intelligence.

## References

  * [1] G. Yenduri _et al._ , “Generative pre-trained transformer: A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions,” _arXiv preprint arXiv:2305.10435_ , 2023.
  * [2] L. Bariah _et al._ , “Large generative ai models for telecom: The next big thing?” _IEEE Communications Magazine_ , 2024.
  * [3] C. Hsieh _et al._ , “Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes,” in _Findings of the Association for Computational Linguistics, 2023_.
  * [4] W. Kwon _et al._ , “Efficient memory management for large language model serving with paged attention,” in _29th Symposium on Operating Systems Principles_ , 2023.
  * [5] T. Dettmers, A. Pagnoni _et al._ , “QLoRA: Efficient finetuning of quantized LLMs,” _arXiv preprint arXiv:2305.14314_ , 2023.
  * [6] L. Wang _et al._ , “A survey on large language model based autonomous agents,” _arXiv preprint arXiv:2308.11432_ , 2023.
  * [7] J. Li, Q. Zhang, Y. Yu, Q. Fu, and D. Ye, “More agents is all you need,” _arXiv preprint arXiv:2402.05120_ , 2024.
  * [8] G. Li, H. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem, “CAMEL: Communicative agents for “mind” exploration of large scale language model society,” _arXiv preprint arXiv:2303.17760_ , 2023.
  * [9] J. S. Park _et al._ , “Generative agents: Interactive simulacra of human behavior,” in _36th Annual ACM Symposium on User Interface Software and Technology_ , 2023.
  * [10] R. Girdhar, A. El-Nouby _et al._ , “ImageBind one embedding space to bind them all,” in _IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2023.
  * [11] J. Wei, X. Wang _et al._ , “Chain-of-thought prompting elicits reasoning in large language models,” in _Annual Conference on Neural Information Processing Systems_ , 2022.
  * [12] H. Gilbert, M. Sandborn _et al._ , “Semantic compression with large language models,” in _International Conference on Social Networks Analysis, Management and Security_ , 2023.
  * [13] A. Maatouk, F. Ayed _et al._ , “TeleQnA: A benchmark dataset to assess large language models telecommunications knowledge,” _arXiv preprint arXiv:2310.15051_ , 2023.
  * [14] A. Dawid and Y. LeCun, “Introduction to latent variable energy-based models: A path towards autonomous machine intelligence,” _arXiv preprint arXiv:2306.02572_ , 2023.
  * [15] M. Assran, Q. Duval _et al._ , “Self-supervised learning from images with a joint-embedding predictive architecture,” in _IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2023.

## Biographies

Hang Zou<EMAIL_ADDRESS>is a Researcher at Technology Innovation Institute,
UAE.

Qiyang Zhao<EMAIL_ADDRESS>is a Principal Researcher at Technology
Innovation Institute, UAE.

Lina Bariah<EMAIL_ADDRESS>is an Adjunct Professor at Khalifa
University, UAE.

Yu Tian<EMAIL_ADDRESS>is a Researcher at Technology Innovation Institute,
UAE.

Mehdi Bennis<EMAIL_ADDRESS>is a Professor at University of Oulu,
Finland.

Samson Lasaulce<EMAIL_ADDRESS>is a Chief Research Scientist with
Khalifa University (UAE) and a CNRS Director of Research (France).

Mérouane Debbah<EMAIL_ADDRESS>is a Professor at Khalifa
University and a Senior Advisor at Technology Innovation Institute, UAE.

Faouzi Bader<EMAIL_ADDRESS>is the Senior Director of Telecom
Unit at Technology Innovation Institute, UAE.

  *[GenAI]: generative artificial intelligence
  *[AGI]: artificial general intelligence
  *[QoS]: quality of service
  *[LLMs]: large language model
  *[LLM]: large language model
  *[UEs]: user equipment
  *[UE]: user equipment
  *[BS]: base station
  *[CoTs]: chain-of-thought
  *[KG]: knowledge graph
  *[TE]: topological embedding
  *[KB]: knowledgebase