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Forgetting in hyperbolic continual learning is driven by semantic drift and hierarchical distortion, revealing critical insights for preserving multimodal representations.
Incomplete modalities can lead to significant distortions in multimodal recommendations, but CaIRec effectively calibrates item representations to enhance performance and coherence.
FreeAct boosts quantized LLM performance by dynamically adapting activation transformations to different token types, moving beyond the static transformations that limit existing methods.
Achieve multilingual LLM safety alignment without expensive language-specific training data by enforcing cross-lingual consistency during monolingual alignment.
