This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains, and offers a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic.
Data-Adaptive Lower-Rank Adaptation (DALorRA), a simple and effective variational Bayesian sparse framework that shifts the paradigm of uncertainty quantification from the dense parameter space to the lightweight rank level of low-rank adaptation (LoRA).
Ji-Jie Zhang, Zhenjiang Ren, Quan Zhang et al.· arXiv.org· 0 citations
This work revisits client drift from a novel frequency-domain perspective and uncovers a critical Spectral Bias of Drift: inter-client gradient divergence is predominantly concentrated in low-frequency components which encode client-specific distributional shifts, while high-frequency components representing fine-grained features remain relatively consistent.
Liyang Yuan, Yibo Yang, Dandan Guo et al.· 0 citations
Experimental evidence from a new perspective, the frequency domain, for SAM perturbations in federated settings is provided, revealing that inter-client perturbation inconsistencies are predominantly concentrated in the low-frequency spectrum.
Liyang Yuan, Yibo Yang, Dandan Guo· 0 citations
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