StereoFactory merges stereo matching foundation models via genetic subset search followed by CMA-ES module routing, reporting lower average errors on four benchmarks than baselines while using 2.7-3.7% of retraining time.
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LFPM mitigates backdoors in model merging by optimizing an anti-backdoor task vector in feature space under the Cross-Task Linearity framework to suppress backdoors without major clean-task degradation.
Language models can use a two-stage sleep process of upward distillation for memory consolidation and RL-based dreaming for unsupervised self-improvement to enable continual learning.
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StereoFactory: A Unified Merging Framework for Robust Stereo Matching
StereoFactory merges stereo matching foundation models via genetic subset search followed by CMA-ES module routing, reporting lower average errors on four benchmarks than baselines while using 2.7-3.7% of retraining time.