Addressable Recall Compaction for Long Context-Window Control in AI Agents
Long-horizon LLM agents accumulate reasoning traces, actions, and tool observations that can eventually exceed a model's fixed context window. Existing compaction methods address this limitation by discarding, summarizing, or retrieving earlier information, but they may remove task-critical details or fail to recover them reliably. We propose ARC (Addressable Recall Compaction), a context-management framework that separates archival storage from active-context presentation. ARC stores tool observations in an append-only, ID-addressable log and replaces older observations with compact citations when compaction is required. The agent can subsequently use these identifiers to request stored ...
| Authors | Thang Dang, Yuma Ichikawa, Sakina Fatima et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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