A Survey on Context Injection Strategies for Long-Context Language Models: Three Perspectives
TL;DR
This survey argues that context injection strategy, rather than context capacity, is the defining research challenge for long-context LLM deployment, and proposes a three-axis analytical framework revealing that injection performance is jointly governed by selection, representation, and scheduling.
Abstract
The performance of large language models (LLMs) is fundamentally shaped by what information reaches them at inference time. As context windows expanded, the challenge shifted from can we fit the data to how do we fit the right data, efficiently and faithfully. This survey argues that context injection strategy, rather than context capacity, is the defining research challenge for long-context LLM deployment. We organize the literature into five major families - direct injection, compression-based injection, retrieval-based injection, memory-based injection, and hybrid/agentic injection. Our primary contribution is a three-axis analytical framework revealing that injection performance is jointly governed by selection (what information is chosen), representation (how it is encoded), and scheduling (when it is delivered during inference). Systematic analysis of the surveyed systems shows that current approaches overwhelmingly optimize the selection axis while representation and scheduling remain largely unexplored. We further identify six structural open challenges that are unlikely to be resolved within any single paradigm, and discuss evaluation gaps that current benchmarks fail to address. These findings suggest that progress on representation and scheduling axes, rather than further scaling of context windows, is the most promising direction for closing the gap between nominal and effective context capacity in deployed LLM systems.