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#machine learning Preprint Oct 2026

DRIVE: Fine-tuning via Data Contribution- and Diversity-aware Weighting with Prior Regularization

Single-stage fine-tuning is pervasive, yet catastrophic forgetting persists when the source pre-training data is inaccessible. Data weighting affords a natural lever to modulate sample influence. Yet existing weighting methods lack a principled mechanism to explicitly arbitrate knowledge retention against new-task adap...

Qing Liu, Xin-Rui Chen, Yi Du et al. · 0 citations
#artificial intelligence Preprint Sep 2026

One Distribution, Two Channels: Coverage-Calibrated KV Retention and Compensation

Long-context decoding is increasingly constrained by key--value (KV) cache memory and bandwidth. Existing fixed-budget compression methods typically separate retention from compensation, while a retention ranking specifies neither discarded attention mass nor the direction of induced output error. We start from an exac...

Shu-Xin Liu, Qing Liu, Yi Du et al. · 0 citations

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