DiLaDiff proposes a latent-augmented masked diffusion language model with consistency distillation that improves quality and accelerates inference by generating continuous latents in negligible time.
A blackboard multi-agent framework lets autonomous agents volunteer for data-discovery tasks, boosting end-to-end success by 13%-57% over rigid master-slave baselines.
Activation patching's natural indirect effect embeds hidden interaction effects between components, which cause conditional importance to be invisible or inflated, explain faithfulness instability, scale with activation distance, and diagnose when greedy component ranking misses combinatorial mechan