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TTME is introduced, a plug-and-play test-time adaptation method that continually updates the external memory by distilling the experience to guide subsequent discovery without updating model weights, and established as an effective and computationally efficient alternative to TTT in the evaluated discovery settings.
This work proposes a self-contained two-stage training method, requiring no distillation from a stronger model, no manual annotation, and no pre-existing ICW IF ability, and introduces $\mathsf{ICWBench}$, a benchmark of three verifiable ICW instruction families, each scored on both detectability and answer quality.
This work introduces a novel approach, \textit{CanaryTrace}, to safeguard the ownership of text datasets and effectively detect unauthorized use by RA-LLMs, and demonstrates high query efficiency, detectability, and consistency, along with minimal perturbation to the original dataset, all without compromising the perfo...
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