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CASTER introduces a gradient-free test-time adaptation method for frozen models, using affine statistics transport and a certificate to decide when to apply adaptation for improved performance without model updates.
This paper presents a negative result showing that current knowledge-editing benchmarks cannot effectively evaluate scope classifiers, using INLAY, a gradient-free editor, to demonstrate that no per-query routing method can improve performance due to structural limitations in the benchmarks.
Introduces GROM, a gradient-free one-shot machine unlearning method that computes a closed-form additive weight update via ridge-regularized least squares, achieving state-of-the-art forgetting-utility trade-offs on benchmarks like TOFU and WMDP, and resisting quantization-based recovery attacks.
This paper proposes CoRA, a gradient-free framework for task-conditioned retrieval in on-device in-context learning, using frozen encoders and closed-form ridge regression to build compact retrieval bases without fine-tuning or backpropagation.
Introduces Eggroll, a low-rank evolution strategy for gradient-free training of spiking neural networks, reducing memory and time overhead while achieving competitive accuracy on N-MNIST.
NVIDIA and Oxford University introduced EGGROLL, a scalable evolution strategies algorithm that trains billion-parameter models without backpropagation, using only integers and parallel mutations.
This paper introduces GUARD-IT, a training-free method for machine unlearning that uses input-dependent activation steering at inference time to remove targeted knowledge from LLMs without modifying weights, matching or exceeding gradient-based baselines while preserving utility and robustness to quantization.