Human Activity Recognition (HAR) on memory-constrained edge devices demands heterogeneous, accurate, resource-adaptive architectures that are costly to design manually. Neural Architecture Search (NAS) provides an automated solution for model design, and its integration with quantization can further expose high-quality architectures under edge memory budgets. Recent NAS increasingly adopts LLM-based methods, including prompt-engineering and fine-tuning approaches. Prompt-engineering approaches suffer from 4-10 hours generation time, while fine-tuning approaches achieve minute-level generation but often sacrifice architecture quality due to the complexity of quantization. We propose QuaNAG, a Neural Architecture Generation (NAG) framework built on the insight that quantization is not an ordinary architecture attribute: it can change model feasibility, alter accuracy, and expand the feasible search space. QuaNAG therefore models quantization as branch-level search decisions and transfers quantization-aware search knowledge into an LLM through curriculum learning. Experiments on demanding HAR benchmarks over five baselines, including in-domain evaluations, out-of-domain evaluations, and on-device experiments, demonstrate that QuaNAG achieves 8.93% average accuracy improvement with several minutes generation time, satisfying all deployment requirements. Our code and model are available at https://github.com/pyq623/QuaNAG.

The increasing prevalence of embedded systems has necessitated manufacturers to migrate product code, transferring existing products to new embedded operating systems (OSes) for getting better compatibility and performance. Since manufacturers’ product code predominantly employs the Thing Specification Language (TSL) paradigm for cloud connectivity, migrated code consequently adheres to the same TSL standard. However, embedded code migration under the TSL paradigm proves more complex than conventional code migration. Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems. There exists a growing demand for a algorithm tailored to TSL paradigm embedded code migration. In response to this demand, we have developed IoTMigrator that employs a multi-agent pipeline to handle the issue. The key insight of our algorithm is the TSL enhancer, specifically designed for the characteristics of the TSL paradigm, which serves as a crucial component in the agent pipeline.To demonstrate the superiority of our algorithm, we have established our own benchmark, which includes six tasks across two OSes, RIOT and Zephyr. We adopted two key metrics: compilation pass rate and task completeness score. The experiment results show that our algorithm outperforms the baseline by an average of at least 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT, and at least 83.4% for pass rate and 18.4% for completeness in Zephyr. This work will be open-sourced in the future.