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TRACER balances the Stability-Plasticity-Cognitivity trilemma, leading to a significant performance boost in continual recommendation systems.
Grafting trainable components onto frozen teacher models reveals the hidden pitfalls of generalization in sequential recommenders, leading to substantial performance gains.
Self-attention in recommendation systems overlooks crucial item relations, but PRISM's multi-perspective approach reveals hidden user preferences that boost performance.
Valid reasoning steps can still be inefficient, leading to a 31-53% increase in token usage without improving outcomes.
LLMs waste context on redundant information when making recommendations; selectively augmenting only lesser-known items boosts accuracy and efficiency.
Achieve ensemble-level sequential recommendation performance with a single network at inference time by distilling diversity from a modular ensemble during training.
Naive fine-tuning of VLMs for multimodal sequential recommendation causes catastrophic modality collapse, but can be fixed with gradient rebalancing and cross-modal regularization.
