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DNAlign is a lightweight alignment framework that integrates control-theoretic optimization with null-space projection and restricts these perturbations to the harmful-related subspace derived from neutral hidden states, thereby preserving general knowledge and response quality.
Results show that probing accuracy is a poor heuristic for selecting intervention layers and suggest a cross-architecture mid-to-late selection criterion, and identify a cross-modal emotion subspace organized by valence and arousal.
PhysMLLMs is a training-stage prior injection architecture that injects physics-inspired spatial continuity priors into Video MLLMs, demonstrating that the injected spatial prior improves video consistency without compromising image-level grounding or general multimodal capability.
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