[KongchangAI]
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Replay

An offline scoring mechanism that substitutes pre-recorded model responses (cassettes) for live model calls, ensuring the scoring process is reproducible and deterministic.

Timeline (last 90 days)

Oct 17

Recovery and replay are different: replay re-executes historical steps to rebuild state, which is costly and may trigger side effects; recovery continues directly from persisted state; checkpoints save state snapshots rather than operation logs, which makes recovery faster and safer.

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Oct 9

The fundamental limitation of replay strategies is the stability-plasticity dilemma: the higher the proportion of retained old samples, the better the protection of old tasks but the lower the learning efficiency for new tasks, and storage budgets accumulate continuously over long task sequences.

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Oct 9

The experiments in this article are simplified 2D toy experiments using extremely small-scale networks and only two tasks; task conflicts and weight importance estimation are more complex in real large-scale models.

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Oct 4

Replay refers to the ability to reload historical conversations or Agent execution trajectories and continue execution, commonly used for debugging, resuming from breakpoints, or restoring session state.

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