Why LLMs Refuse to Summarize More in Long Contexts?

Long-horizon agents routinely exceed their context windows. A common form of context compaction that reduces a long interaction history to a smaller representation is to summarize the history and continue from the summary. This summarization loses information, and this loss is a common source of agent failures: forgotten details are rediscovered at extra cost or silently dropped. The natural repair is to ask for a longer summary, so we investigate whether that option actually works. Does summary length grow with input length? Across eleven models spanning coder, general-purpose, and reasoning families, summary length grows far more slowly than the input, so at 100k tokens, less than one percent of the input survives on both long documents and real agent trajectories. Furthermore, prompt-level directives do not close this gap: explicit length directives raise output only modestly, and the summary length plateaus far below the requested amount, however the request is phrased. To test whether training data can move this ceiling, we lightly fine-tune two models on summaries that differ only in length. The resulting output length shifts dramatically in either direction, tracking the fine-tuning summary length, even under a prompt that never mentions length. Consequently, this paper shows that summary length grows strongly sublinearly with input size, is only weakly responsive to length directives in the prompt, and is strongly responsive to fine-tuning, suggesting that training data, rather than the prompt, is the effective lever.