cs.IR · 2026-08-27 · No. 97

Information Retrieval, 2026-08-27.

3 new papers in cs.IR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Data Citation for Large Language Models: A Challenge

    Gianmaria Silvello

    cs.IR · cs.AI · cs.DB · cs.DL

    Large language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further functions, credit and provenance, and it applies to data as much as to text. This paper argues that data citation for large language models is an open challenge, distinct...

    arxiv.org/abs/2608.25663 · PDF

  2. 02

    When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory

    Kazuki Nakayashiki

    cs.IR · cs.AI · cs.CL

    An agent that inherits a consolidated memory may inherit a constraint that was true when written and has since been withdrawn by a newer authoritative record. Under a scarce verification budget, does the agent recover the withdrawal, and if not, is the error avoidable without spending more? We model supersession explicitly -- historical provenance is immutable; what changes is which record is current -- and assign by design the memory's form,...

    arxiv.org/abs/2608.25553 · PDF

  3. 03

    CRAMER: Control via Request-Aware Masking for Editing Recommenders

    Zhiyuan Julian Su, Naihe Feng, Zhen Luther Qin, Ga Wu

    cs.IR · cs.AI · cs.LG

    Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large language models (a.k.a. prompt engineering),...

    arxiv.org/abs/2608.25370 · PDF

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