CVLC fuses calibrated vision prototypes with LLM-generated language prototypes and applies dual coalescent projection plus latent space reservation to enable few-shot adaptation across sequential domains, reporting up to 16% gains over prior methods.
Cl-lora: Continual low-rank adaptation for rehearsal-free class-incremental learning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A plug-and-play KL regularizer that masks the target token and renormalizes probabilities to improve the learning-forgetting trade-off in LoRA adaptation of LLMs.
citing papers explorer
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Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
CVLC fuses calibrated vision prototypes with LLM-generated language prototypes and applies dual coalescent projection plus latent space reservation to enable few-shot adaptation across sequential domains, reporting up to 16% gains over prior methods.