Context Compaction Theory
Large Language Models (LLMs) have a bounded context window. The context window is the maximum input size an LLM can consume for a single inference. AI agents rely on a process called context compaction to fit their state within the context window when calling an LLM. Despite its ubiquity, context compaction has received essentially no formal analysis. In this paper, we initiate a formal study of context compaction. We first introduce a framework consisting of two games that capture the two algorithmic strategies for context compaction used by contemporary AI agents in practice. The Context Selection Game models context compaction algorithms that select a subset of an agent's accumulated s...
| Authors | Hayder Tirmazi, Sam Markelon, Allison Bishop et al. |
|---|---|
| Primary category | cs.DS |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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