Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale
arXiv:2607.01538v1 Announce Type: new Abstract: 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
This research emerges as the capabilities and limitations of large language models for complex tasks like in-context retrieval are being rigorously tested, pushing the boundaries of what these systems can achieve at scale.
A strategic reader should care because advancements in in-context retrieval at million-token scales directly impact the utility and efficiency of AI agents and enterprise knowledge systems, moving beyond simple chatbots to more autonomous reasoning and data synthesis.
This work indicates a potential shift from vector-based retrieval towards in-context learning for information retrieval, suggesting a different architectural approach for future AI applications involving extensive data.
- · AI algorithm developers
- · Companies building knowledge management systems
- · Enterprise AI solutions
- · Traditional vector database providers (if LMs fully displace their core use)
- · Companies reliant on simple keyword search
It directly improves the ability of language models to process and utilize vast amounts of information without explicit fine-tuning.
This could accelerate the development and deployment of more capable and autonomous AI agents that can 'read' and reason over large document sets.
The enhanced in-context retrieval might reduce the need for specialized data infrastructure, consolidating more intelligence within the language model itself, potentially impacting the entire compute supply chain and AI development methodologies.
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Read at arXiv cs.CL