arXiv:2603.16138v2 Announce Type: replace-cross
Abstract: Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these sy...
By Michelle Huang, Agam Goyal, Koustuv Saha, Eshwar Chandrasekharan
arXiv:2605.16428v4 Announce Type: replace-cross
Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence o...
By Peibo Zhang, Ruomeng Cui, Dennis J. Zhang
arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.
By Ronald Sielinski
arXiv:2609.00319v1 Announce Type: cross
Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compos...
By Phuong Anh Nguyen, Jill Noorily, Matthew Flathers, Haruka Notsu, Laura Ospina-Pinillos, Tommy Nguyen, Samantha Clark, Aoife Keane, Grace Thompson, John Torous
The paper demonstrates that large language models (LLMs) used for forecasting real‑world events can be manipulated by simply publishing new articles, even without direct access to the model or its retriever. By injecting a small number of targeted news pieces into a common crawl corpus, an adversary can flip over half of the forecast probabilities and significantly degrade forecast accuracy. The study also shows that common defense strategies can be cheaply bypassed, highlighting the vulnerability of probabilistic LLM judgments to information‑supply‑chain attacks.
By Yuan Lu, Yukuan Zhang
arXiv:2606. 12439v1 Announce Type: cross Abstract: Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers.
By Yizhu Wen, Nan Zhang, Haohan Yuan, Xun Chen, Haopeng Zhang, Hanqing Guo