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This paper proposes a framework to evaluate and improve faithfulness of chain-of-thought reasoning by controlling information flow, using entropy-based, KL-divergence, and gradient-based diagnostics, and introduces training interventions (attention masking, gradient masking, adversarial perturbations) that make reasoning more transparent and reduce shortcut reliance.
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This paper proposes Diffusion-Adaptive Routing (DAR), a learnable, timestep-adaptive residual replacement that improves cross-layer information flow in Diffusion Transformers, leading to significant training acceleration and quality improvements.
This paper investigates how semantic information is distributed across textual tokens in text-to-image models, finding that information concentration and cross-item interactions significantly affect image generation alignment. The authors use patching techniques to demonstrate that simple encoding-stage interventions can improve alignment quality.