# LAMBO: Large AI Model Empowered Edge Intelligence

arXiv: https://arxiv.org/abs/2308.15078 · HTML: https://mlsanity.com/paper/2308.15078/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [Fine-grained Retrieval Prompt Tuning](https://mlsanity.com/paper/2207.14465/) (2022)

## Built on by (most cited first, 6 of 6)

- [Large Language Models for Education: A Survey](https://mlsanity.com/paper/2405.13001/) (2024)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [Visual Fidelity Index for Generative Semantic Communications with Critical Information Embedding](https://mlsanity.com/paper/2505.10405/) (2025)
- [Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2310.17492/) (2023)
- [Attention-based UAV Trajectory Optimization for Wireless Power Transfer-assisted IoT Systems](https://mlsanity.com/paper/2502.17517/) (2025)

## Abstract (arXiv)

Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. However, traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. In this paper, we propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems. We first use input embedding (IE) to achieve normalized feature representation with heterogeneous constraints and task prompts. Then, we introduce a novel asymmetric encoder-decoder (AED) as the decision-making model, which is an improved transformer architecture consisting of a deep encoder and a shallow decoder for global perception and decision. Next, actor-critic learning (ACL) is used to pre-train the AED for different optimization tasks under corresponding prompts, enhancing the AED's generalization in multi-task scenarios. Finally, we propose an active learning from expert feedback (ALEF) method to fine-tune the decoder of the AED for tracking changes in dynamic environments. Our simulation results validate the advantages of the proposed LAMBO framework.