Context Compaction Cuts Costs: "You Don't Need Long Context"

Belcak, Muralidharan, Jiang, Fu, Byeon, Ye, Angilly, Taghibakhshi, Liu, Ouyang, Heinrich, Molchanov (NVIDIA) · arXiv, Nov 2026
arXiv 2611.05306 Interactive page
MEASURED servingBENCHMARK accuracyMODELED dollarsILLUSTRATIVE schematic

Three agent regimes Illustrative

Live context length as an agent works. Schematic shapes only; no values are paper data.

Why the paper splits the root question Illustrative

Why later tokens cost more Illustrative

Each new token reads the KV entries of every earlier token. Hover a row to count keys read.
Hover a cell.

Measured H100 throughput gap, 16K vs 256K Measured

Peak decode throughput before KV eviction. Repeat runs varied ≤2%. Qwen's "~7" is approximate in the episode.

From measurement to "estimated optimized retail cost" Modeled

Straight line from full-window retail price down to 0.20× at 16K, clamped below 16K (Appendix B as described in the episode). Neither closed model was measured by depth.

Same tokens, two price lists

Accuracy change ranges Benchmark

Bars span the reported min to max change in points vs full-context baseline. Single runs, no confidence intervals.

Single-action compaction: mixed evidence Benchmark

Where the state lives

Evidence ledger: what is measured vs modeled

Lineage Conceptual

References