@inproceedings{xian-etal-2025-measuring,
    title = "Measuring temporal effects of agent knowledge by date-controlled tool use",
    author = "Xian, R. Patrick  and
      Cui, Qiming  and
      Bauer, Stefan  and
      Abbasi-Asl, Reza",
    editor = "Kamalloo, Ehsan  and
      Gontier, Nicolas  and
      Lu, Xing Han  and
      Dziri, Nouha  and
      Murty, Shikhar  and
      Lacoste, Alexandre",
    booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.realm-1.25/",
    doi = "10.18653/v1/2025.realm-1.25",
    pages = "346--355",
    ISBN = "979-8-89176-264-0",
    abstract = "Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as the grounding for agent knowledge, yet an improper configuration affects the quality of the agent{'}s responses. Here, we assess the agent behavior using distinct date-controlled tools (DCTs) as a stress test to measure the knowledge variability of large language model (LLM) agents. We demonstrate the temporal effects of an LLM agent as a writing assistant, which uses web search to complete scientific publication abstracts. We show that the temporality of search engines translates into tool-dependent agent performance but can be alleviated with base model choice and explicit reasoning instructions such as chain-of-thought prompting. Our results indicate that agent design and evaluations should take a dynamical view and implement effective measures to account for the temporal influence of external resources to improve agent reliability."
}