Scroll: Programmatic Context Management for Long-Horizon Agents

Long-horizon agents for complex software engineering and deep research accumulate actions, observations, and intermediate state far beyond a model's context window. Context management must therefore efficiently construct compact, task-relevant views of this growing history for each model call, even though future information needs are unknown. Existing methods typically either bound context growth through structured but lossy compaction and extraction, or externalize the full history as raw, unstructured context for model-written programs to navigate. We present $Scroll$, a lossless and programmable context manager for long-running agent sessions. An append-only event log retains history losslessly under stable addresses and timestamps, while a tiered eviction index maintains in-context address-based anchors to off-context session history. Model-written programs access history through structured search and recovery operations, compute over it in a sandboxed persistent Python kernel, and expose only selected results. With Qwen3.8-Max, Scroll scores **94.8** on LongMemEval$\_S$, **75.5** on BEAM$\_10M$, and **93.3** on LOCA$\_256K$, exceeding the best published scores by **7.5** points on BEAM$\_10M$ and **44.0** points on LOCA$\_256K$. Ablations on BEAM$\_10M$ show that lossless retention, the persistent Python kernel, and the structured access layer make complementary contributions to accurate and token-efficient context construction.