Decision-OS V11: Forget for Future — Reconnectable Forgetting for Long-Horizon Agentic AI

Long-horizon AI agents increasingly depend on persistent memory, context management, retrieval, summarization, and handoff across sessions. However, more memory does not automatically produce better judgment: long histories can create context burden, premise drift, stale assumptions, and unsafe reuse of compressed summaries.

Decision-OS V11 introduces Reconnectable Forgetting as a memory-governance framework for this problem. It addresses a current operational pain in long-running AI agents: as histories accumulate across conversations, files, tool calls, summaries, code changes, and handoffs, memory becomes both a capability and a burden.

The paper argues that self-evolving systems do not require perfect memory. They require the ability to forget in a way that remains reconnectable. Reconnectable Forgetting treats forgetting not as deletion, loss, or ordinary summarization, but as controlled compression that preserves future re-entry through evidence anchors, As-of conditions, stop/recheck conditions, unresolved deltas, provenance keys, and re-entry paths.

The framework defines a three-layer memory architecture: Active Context, Compressed Residue, and Provenance-Key Layer. It further introduces Decision-Equivalent Compression, Judgment Fidelity, an Outcome-Masked Replay Test, and a Reconnectability Gate with PASS/DELAY/BLOCK outputs.

V11 also defines practical controls for adaptive context handoff, impact-scaled fidelity, recompression stopping, evaluation budgeting, and Discovery Lane handling for low-impact residues. The goal is to reduce context burden without allowing compressed memory to gain false judgment authority.

This work should be read as a conceptual and operational framework, not as a complete memory implementation or empirical validation report.