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@@ -92,7 +92,8 @@ The current value of Key1 is Value_N.
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  LLMs cannot reliably retrieve Value_N. Distribution spans value_1 to value_N, and as N increases, the answers skew increasingly toward value_1.
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- **Note**:We **randomize** update order to mimic unpredictable changes. Counterintuitively, this often helps LLMs, since the final update usually lands near the end of the context; And in the sequential setting, most models lose track after only a few updates—even with 5–8k-token inputs.(sequential mode dataset provided separately)
 
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  ## Why this is challenging for LLMs:
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  - Multiple co-references to the same key cause strong interference.
 
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  LLMs cannot reliably retrieve Value_N. Distribution spans value_1 to value_N, and as N increases, the answers skew increasingly toward value_1.
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+ ## Note:
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+ We **RANDOMIZE** update order after generation to mimic unpredictable changes. Counterintuitively, this often helps LLMs, since the final update usually lands near the end of the context; And in the sequential setting, most smaller **(less than arouond 600B)** models lose track after only a few updates—even with 5–8k-token inputs.(sequential mode dataset provided separately)
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  ## Why this is challenging for LLMs:
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  - Multiple co-references to the same key cause strong interference.