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GenView is presented, a controllable framework that augments the diversity of positive views leveraging the power of pretrained generative models while preserving semantics, and an adaptive view generation method that dynamically adjusts the noise level in sampling to ensure the preservation of essential semantic meani...
DG-Mem is proposed, a dual-grained agentic memory framework that augments a frozen MLLM with a non-parametric, externally stored memory built once from training-time rollouts and consulted read-only at test time, and improves consistently over no-memory and competitive memory baselines.
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-grain...
This work proposes HyperSafe, a framework that restores safety behavior by generating a model-specific Safe Side Network (SSN) for each fine-tuned checkpoint by using layer-wise activation fingerprints to capture how fine-tuning changes the model's inner representations.
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