Agent is an old name, but the importance of agent in this AI era has been well recoganized. An agent can be viewed as a neural-symbolic approach, as discussed here, but the implication goes far beyond this view. It is worth noting that Agent will influence the future of systems. For example, will the future LLM serving systems look more like Parrot (Lin et al., 2024), or do we need to build new RL simulators like Android Arena (Xing et al., 2024) to enable OS agent?
@inproceedings{parrot24,title={Parrot: Efficient Serving of LLM-based Applications with Semantic Variable},author={Lin, Chaofan and Han, Zhenhua and Zhang, Chengruidong and Yang, Yuqing and Yang, Fan and Chen, Chen and Qiu, Lili},year={2024},booktitle={18th {USENIX} Symposium on Operating Systems Design and Implementation, {OSDI}},}
KDD
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
Mingzhe Xing, and 5 more authors
In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 2024
Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.
@inproceedings{10.1145/3637528.3671650,title={Understanding the Weakness of Large Language Model Agents within a Complex Android Environment},author={Xing, Mingzhe and Zhang, Rongkai and Xue, Hui and Chen, Qi and Yang, Fan and Xiao, Zhen},year={2024},booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},location={Barcelona, Spain},publisher={Association for Computing Machinery},address={New York, NY, USA},series={KDD '24},pages={6061–6072},doi={10.1145/3637528.3671650},isbn={9798400704901},numpages={12},keywords={ai agent, large language model, task planning}}