Can AI Agents be benchmarked within strictly controlled simulated trading environments to investigate how external factors impact their collective trading behaviors? These factors, which frequently influence trading behavior, are critical elements in the quest to maximize investors’ profits. Our work aims to address th...
Chong Zhang, Xinyi Liu, Zhongmou Zhang et al.· ACM Transactions on Intellig...· 0 citations
The workshop brings together researchers and practitioners from data mining, LLMs, NLP, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD.
Xiaoyan Zhao, Yang Zhang, Mo-Xin Li et al.· Proceedings of the 32nd ACM...· 0 citations
This workshop aims to bring together researchers and practitioners to discuss recent advances, practical applications, and future directions of AI agents in information retrieval, while encouraging collaboration and knowledge exchange within the community.
Qing-Song Wen, P. Mehrotra, Yongfeng Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces Agentic Commerce World (ACWorld), an environment for evaluating such agents across ongoing transactions, and validates agent actions before updating shared transaction state and records the resulting interactions, making agent behavior auditable and evaluation reproducible.
Shi-Chen Fan, Ming-Dai Yang, Duo Wang et al.· 2 citations· ⚡1
Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI...
Xiaoyan Zhao, Yang Zhang, Moxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
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