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ResKV is proposed, which divides a fixed KV budget into an exact main cache and a compact residual cache that reconstructs the contribution of omitted tokens and lets main-cache tokens and residual entries participate in the same softmax normalization.
Experiments show that MoRAL consistently outperforms statistical and LLM-based baselines under the GEO-BLEU metric, improving the mean score over the strongest baseline by 5.82% and achieving a 2.6 times reduction in per-user inference time relative to recent LLM baselines using the same backbone.
MoCaRS is proposed, a retrieval and selection framework conditioned on motivation that reformulates target day generation and substantially reduces copying-induced overlap while retaining reasonable mobility fidelity.
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