Language models (LMs) raise an intriguing alternative to vector-based retrieval: conditioning on an in-context corpus and directly generating a relevant answer. However, prior work has largely focused on proprietary systems or the smaller-scale reranking task, leaving corpus-scale in-context retrieval largely unexplored. In this work, we present the first systematic study of in-context retrieval on two scales practical retrievers demand: million-token corpora and length-generalization far beyond training-time sizes. We first introduce BLOCKSEARCH, an 0.6B LCLM retriever whose architectural and training modifications improve over prior LM baselines and length-generalize up to 10x beyond its training regime. Nevertheless, its retrieval still collapses under more extreme extrapolation. We trace this failure to an attention dilution effect: as the corpus grows, irrelevant documents dominate the softmax denominator and the normalized mass on the gold document collapses. Individual attention heads continue to locate the gold document more reliably than the model decodes it, even at million-token scale, though this signal also weakens as the corpus grows. Motivated by this analysis, we introduce length-aware adjustments to the attention softmax and document-level sparse attention, improving retrieval at million-token scale to performance comparable to a same-backbone dense retriever. On the lexical LIMIT benchmark, these gains also transfer out of distribution: at a million-token corpus, Recall@1 reaches 13.5% versus 2.9% for the dense baseline. Together, our results position in-context retrieval a promising alternative to classical retrieval while emphasizing attention control under extreme context growth as a new challenge.

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