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Scale gated self-editing to real-time and streaming environments

Scale the gated continual self-editing framework to real-time and streaming environments and demonstrate efficiency and robustness under continuous inputs.

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Background

While the proposed gating approach mitigates forgetting in controlled, batch-style SQuAD-like settings, the authors recognize that deployment scenarios often involve continuous, streaming inputs where timely decisions are required.

Adapting the framework to real-time conditions entails addressing computational overheads of gating, ensuring stable behavior under non-stationary input streams, and maintaining robustness and efficiency during continual updates without batch evaluation windows.

References

Building on this foundation, several extensions remain open. Another is scaling to real time and streaming environments, where efficiency and robustness under continuous inputs become critical.

STABLE: Gated Continual Learning for Large Language Models (2510.16089 - Hoy et al., 17 Oct 2025) in Section 6.3 (Future Work)