Large Artificial Intelligence Models (LAMs)

• Large Artificial Intelligence Models (LAMs) are expansive neural networks with 10^8 to 10^12 parameters, leveraging deep transformer stacks and massive multi-modal pre-training.
• They enable paradigm shifts in wireless communications, weather forecasting, molecular modeling, and multi-agent intelligence through robust generalization and multimodal integration.
• Key challenges include efficient deployment, parameter-efficient fine-tuning, and model compression to harness their full potential across diverse, resource-constrained environments.

Large Artificial Intelligence Models (LAMs) are neural architectures characterized by vast parameter counts—ranging from hundreds of millions to trillions—deep, uniform structures (primarily multi-layer Transformers), and large-scale pre-training over diverse, heterogeneous data. LAMs exhibit robust generalization, multitask learning, and multimodal integration, enabling paradigm shifts across domains such as wireless communications, multi-agent intelligence, weather forecasting, molecular modeling, and embodied cyber-physical systems. This entry synthesizes foundational principles, typical architectures, deployment frameworks, representative applications, and outstanding challenges, with emphasis on the wireless physical layer and adjacent fields.

1. Formal Definition and Distinction from Conventional AI Approaches

LAMs are defined as neural networks exhibiting:

• Parameter scale: $10^8$–$10^{12}$ parameters.
• Deep, regularized Transformer-based architectures (often 10s to 100s of blocks).
• Pre-training on massive datasets (multi-terabyte, multi-modal) spanning text, vision, speech, tracks, or sensor modalities (Grattafiori et al., 31 Jul 2024, Guo et al., 4 Aug 2025).
• Few-shot, cross-task adaptation and emergent reasoning at large scale.

By contrast, conventional AI in communications and physical modeling has relied on compact, task-specific models: shallow autoencoders, convolutional nets, recurrent nets, each trained from scratch on narrow datasets (typically $10^4$–$10^6$ parameters and single-task supervised objectives). LAMs address four major shortcomings of these traditional approaches: model complexity, poor generalization to unseen scenarios, algorithmic rigidity, and inability to handle multiple data modalities (Guo et al., 4 Aug 2025, Jiang et al., 6 May 2025).

Characteristic	Conventional AI	LAMs
Scale	$10^4$–$10^6$ params	$10^8$–$10^{12}$ params
Architecture	Bespoke, mixed-block	Uniform Transformer stacks
Data usage	Single-task, limited	Multitask, massive-scale
Generalization	Narrow, weak	Few-shot, cross-task
Multimodal processing	Minimal/absent	Native, strong

2. Core Architectures: Transformers, Attention, and Scaling Laws

Virtually all current LAMs are based on the Transformer block: $$\text{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{Q K^\top}{\sqrt{d_k}}\right) V$$ where $Q, K, V \in \mathbb{R}^{n \times d}$ denote query, key, and value matrices; $d_k$ is the key dimension. Multi-head attention enhances representational capacity: $$\text{head}_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V),\quad \text{MultiHead}(Q, K, V) = \mathrm{Concat}(\text{head}_1, ..., \text{head}_h) W^O$$ Each block augments attention with feedforward layers, residual connections, and normalization. Position encoding—typically either learned or via rotary/relative schemes—allows handling of sequence data, including extremely long contexts (e.g., up to 128K tokens in Llama 3) (Grattafiori et al., 31 Jul 2024).

Scaling laws govern the empirical loss as a function of model parameters ($P$) and dataset size ($D$): $L(P, D) \approx \alpha P^{-\beta} + \gamma D^{-\delta} + \mathrm{const} \qquad\text{with %%%%12%%%%–%%%%13%%%%}$ This sublinear improvement is the fundamental driver towards larger models, underlining capacity gain through parameter and data explosion (Guo et al., 4 Aug 2025, Grattafiori et al., 31 Jul 2024).

3. Training Paradigms, Adaptation, and Deployment Strategies

LAMs offer two principal strategies for downstream adoption in specialized domains:

3.1 Leveraging Pre-trained LAMs

• Framework: Pre-trained LAMs (LLMs, LVMs) are adapted using task-specific heads and minimal fine-tuning (often via parameter-efficient methods such as LoRA or prompt tuning).
• Loss functions: Task regression (MSE), classification (cross-entropy), masked modeling for self-supervised objectives:
  • $$L_\text{MSE} = \mathbb{E}[\|H_{\text{true}} - H_{\text{pred}}\|_F^2]$$
  • $L_\text{CE} = -\sum_{i=1}^K y_i \log p_i$ (Guo et al., 4 Aug 2025, Jiang et al., 28 May 2025)
• Example: LLM4CP adapts GPT-2 for channel prediction by tokenizing complex CSI matrices and regressing future channels, achieving 20–40% NMSE reduction with sub-10 ms latency (Guo et al., 4 Aug 2025).

3.2 Native LAMs

• Framework: Entirely new transformer architectures, self-supervised on domain-specific, large-scale data (e.g., channels, sensor time series) with masked modeling or contrastive objectives.
• Adaption: After pretraining, lightweight heads are attached for downstream tasks (e.g., beam prediction, signal detection), with downstream fine-tuning (Guo et al., 4 Aug 2025, Jiang et al., 6 May 2025).

3.3 Model Compression and Conditional Computation

• Knowledge distillation, structured pruning, and quantization for deploying on resource-limited hardware (Lyu et al., 28 May 2025, Guo et al., 4 Aug 2025).
• Sparsity and mixture-of-experts (MoE): Activate only a subset of subnetworks per input to control compute cost.
• Collaborative paradigm: LAMs serve as fixed "teachers" or knowledge bases, lightweight student models (SAMs) provide rapid adaptation—enabling environment-specific tuning without large-scale retraining (e.g., LASCO/E-LASCO for CSI feedback) (Cui et al., 13 Dec 2025).

Deployment Strategy	CPU/GPU Memory	Data Required	Typical Usage
Full Fine-Tuning (LAM)	Very high	Large	Global update, rare use
LoRA / Prompt (PEFT)	Low	Medium	Fast domain adaptation
LAM–SAM Collaboration	Minimal	Very low	Rapid, environment-specific

4. Representative Applications Across Domains

LAMs have demonstrated concrete advances in the following wireless and scientific areas:

4.1 Physical-Layer Wireless Communication

• Channel and CSI Prediction: LLM4CP reduces NMSE by 20–40% over GRU/CNN baselines.
• CSI Feedback: Prompt-enabled native LAMs reduce reconstruction NMSE by 30% in unseen wireless environments.
• Multimodal Beam Prediction: M²BeamLLM fuses vision, LiDAR, and channel data to achieve >95% accuracy as compared to ≈85% with channel-only models (Guo et al., 4 Aug 2025).
• Edge Inference and Split Learning: Distributed frameworks such as SFLAM enable ViT-sized LAMs on mobile devices, maintaining high accuracy and reducing total latency/energy by 30–50% (Qiang et al., 12 Apr 2025, Wang et al., 1 May 2025).

4.2 Multi-Agent and Low-Altitude Economy Systems

• Hierarchical Collaboration: LAMs partitioned between cloud (full model), aerial layer (partial modules on UAVs), and ground (small heads) optimize offloading, energy, and latency joint objectives (Lyu et al., 28 May 2025).
• Secure Communications: LAM-augmented RL agents exhibit 15–20% higher secrecy throughput, and 40–50% faster convergence in adversarial LAWN security tasks (Zhang et al., 1 Aug 2025).

4.3 Scientific Modeling

• Weather and Ocean Forecasting: LAMs such as Pangu-Weather, GraphCast, and FuXi extend deterministic skill in 10-day forecasts, outperforming traditional NWP by >20–40% in RMSE, while being 1–2 orders of magnitude faster (Ling et al., 30 Jan 2024).
• Atomic and Materials Simulation: Foundation LAMs (DPA-2) pre-trained on millions of DFT frames offer 2–5× lower zero-shot errors and permit high-fidelity adaptation to new molecules/materials with orders-of-magnitude fewer labeled examples (Zhang et al., 2023).

4.4 Embodied and Agentic Intelligence

• Large Action Models (LAMs): Extend passive LLM capabilities to action generation and execution, supporting perception–decision–action loops in GUIs, robots, and autonomous systems (Wang et al., 13 Dec 2024).
• Integrated Perception–Computation–Communication: LAM-enabled Intelligent Base Station Agents (IBSAs) for 6G fuse multi-modal inputs (RF, vision, lidar), employ closed-loop learning/planning, and execute multi-agent coordination with edge–cloud collaboration (Li et al., 17 Dec 2025).

5. Performance Metrics, Evaluation, and Benchmarks

Metrics and protocols differ by domain, but typically cover:

• Regression error: Normalized Mean Squared Error (NMSE) for reconstruction tasks.
• Classification accuracy: Task-specific, e.g., beam index prediction.
• Latent feature similarity: Generalized Cosine Similarity (GCS) for evaluating reconstructed signals.
• Sample and computational efficiency: Training epochs to convergence and per-operation latency/energy under different split or offloading strategies (Guo et al., 4 Aug 2025, Qiang et al., 12 Apr 2025, Cui et al., 13 Dec 2025).
• Model adaptability: NMSE vs. number of adaptation samples, convergence speed of SAMs/PEFT adapters (Cui et al., 13 Dec 2025).
• Multi-modal performance: Joint accuracy on cross-modal alignment tasks (e.g., perception, tracking, semantic segmentation) (Li et al., 17 Dec 2025).

6. Limitations and Frontier Research Directions

Open challenges focus on the following axes:

1. Efficient Architectures: Adapt MoE, SSMs, and neuromorphic computing to balance model capacity and real-time resource constraints (Guo et al., 4 Aug 2025, Lyu et al., 28 May 2025, Shi et al., 2 Apr 2025).
2. Interpretability: Develop explainable AI for channel-physical data (e.g., attention visualization, LIME/SHAP for complex-valued signals) (Guo et al., 4 Aug 2025, Li et al., 17 Dec 2025).
3. Standardized Datasets and Benchmarks: Establish large-scale, multi-scenario wireless datasets (billions of channel realizations) with metadata—analogues to Common Crawl/ImageNet for wireless—to enable reproducibility and foundation model evaluation (Guo et al., 4 Aug 2025, Shi et al., 2 Apr 2025).
4. Real-Time and Edge Deployment: Hardware–algorithm co-design (low-rank compression, quantization, custom ASIC/FPGA for mixed-precision, sub-millisecond inference) (Wang et al., 1 May 2025, Qiang et al., 12 Apr 2025).
5. LAM–SAM and Hybrid Collaboration: LAMs act as scenario-agnostic teachers, while environment-specific SAMs or LoRA adapters rapidly adapt to local conditions without multi-million parameter retraining (Cui et al., 13 Dec 2025).
6. Security, Trustworthiness, and Resilience: Robustness against adversarial attacks, privacy leakage (DP, federated learning), and explainability for safety-critical applications (Lyu et al., 28 May 2025, Zhang et al., 1 Aug 2025).
7. Continual Adaptation and Multi-Agent Coordination: Real-time field adaptation in non-stationary, multi-agent, and adversarial environments; integration with game-theoretic and graph-based models (Lyu et al., 28 May 2025, Li et al., 17 Dec 2025).

7. Outlook and Synthesis

LAMs redefine both the technological limits and architectural paradigms of intelligent systems, shifting from narrow, brittle, data-hungry models to extensible, robust, and multimodally integrated platforms. In wireless communications and beyond, their adoption portends significant advances in generalization, few-shot transfer, real-time adaptation, and system-level autonomy. Realization of their full capabilities depends critically on new compression strategies, standardized infrastructure, domain-aligned benchmarks, adaptive hardware-software stacks, and safe, interpretable deployment in mission-critical settings (Guo et al., 4 Aug 2025, Jiang et al., 6 May 2025, Li et al., 17 Dec 2025).

---

Key References:

• (Guo et al., 4 Aug 2025) Large AI Models for Wireless Physical Layer
• (Lyu et al., 28 May 2025) Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
• (Zhang et al., 1 Aug 2025) Large AI Model-Enabled Secure Communications in Low-Altitude Wireless Networks
• (Grattafiori et al., 31 Jul 2024) The Llama 3 Herd of Models
• (Qiang et al., 12 Apr 2025) Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
• (Cui et al., 13 Dec 2025) Large and Small Model Collaboration for Air Interface
• (Li et al., 17 Dec 2025) Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network
• (Zhang et al., 2023) DPA-2: a large atomic model as a multi-task learner
• (Wang et al., 13 Dec 2024) Large Action Models: From Inception to Implementation
• (Ling et al., 30 Jan 2024) Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models
• (Wang et al., 1 May 2025) Edge Large AI Models: Revolutionizing 6G Networks
• (Jiang et al., 6 May 2025) A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges
• (Jiang et al., 28 May 2025) From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications