CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.
arXiv preprint arXiv:2302.11289(2023)
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GES framework uses Gaussian-partitioned specialist policies to co-optimize morphology and control for buoyancy-assisted legged robots, reporting 5-25% performance gains, 3x hardware obstacle improvement, and 37% faster design search versus baselines.
QMTL uses shared VQC encoding plus task-specific quantum ansatz heads to achieve linear parameter scaling with the number of tasks while matching or exceeding classical multi-task baselines on three benchmarks.
citing papers explorer
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CURE:Circuit-Aware Unlearning for LLM-based Recommendation
CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.
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Rapid co-design of Buoyancy-assisted robots for Challenging Locomotion using Gaussian Evolutionary Specialists
GES framework uses Gaussian-partitioned specialist policies to co-optimize morphology and control for buoyancy-assisted legged robots, reporting 5-25% performance gains, 3x hardware obstacle improvement, and 37% faster design search versus baselines.
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Parameter-efficient Quantum Multi-task Learning
QMTL uses shared VQC encoding plus task-specific quantum ansatz heads to achieve linear parameter scaling with the number of tasks while matching or exceeding classical multi-task baselines on three benchmarks.