Dynamic Context Runtime: Bounded Attention over Unbounded History
Language models degrade as stale and superseded material piles up in their context. Making the window bigger does not fix it. This report describes a runtime that keeps history unbounded and attention bounded — storing everything as immutable spans and a typed provenance graph, then assembling a small working set each turn by solving a knapsack under an explicit token budget. It includes a zero-dependency Rust implementation, an ablation that names which mechanisms are actually carrying the result, and a scaling table that holds at 4.19 million tokens of history on a lexically varied corpus — alongside an account of why the same measurement on a repetitive one would have meant considerably less.
- 4.19Mtokens of history
- 259tokens per query
- 7 / 7probes answered
- 48,651state nodes