ICICLE: Expanding Retrieval with In-Context Documents

• URL: http://arxiv.org/abs/2605.26902v2
• Date: Wed, 27 May 2026 02:21:28 GMT
• Title: ICICLE: Expanding Retrieval with In-Context Documents
• Abstract summary: Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge.<n>We propose ICICLE, an in-context indexing framework that performs source-aware docid generation over both parametric memory and context-provided document-docid pairs.
• Score: 28.99813139934325
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes corpus expansion costly: adding new documents requires updating model parameters to encode new document-docid associations incurs repeated training and catastrophic forgetting of previously indexed documents. In this work, we revisit incremental GR as an in-context retrieval problem, where newly added documents are supplied as inference-time document-docid evidence. We propose ICICLE, an in-context indexing framework that performs source-aware docid generation over both parametric memory and context-provided document-docid pairs. ICICLE combines a `[COPY]`-based routing mechanism, preference-based calibration, and large context adaptation to distinguish context-grounded retrieval from parametric retrieval. Experiments on MS MARCO and NQ320K show that ICICLE improves retrieval of newly introduced documents while preserving seen-document retention without corpus-specific retraining. Our analysis further shows that high-shot degradation is mainly caused by routing failure, highlighting source-selection calibration as a key bottleneck for scaling in-context generative retrieval.

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