Muon achieves larger one-step loss decreases than Adam via lower curvature penalties driven by reduced normalized directional sharpness rather than update scale, with advantages amplified by data imbalance and within-layer curvature.
VideoMLA replaces per-head video diffusion KV caches with shared low-rank latents to cut memory by 92.7% and improve long-horizon streaming quality and throughput.
Formalizing discretion as a dynamic budget problem yields time-dependent override thresholds and shape-dependent spending rates, with homelessness data showing budget-constrained discretionary patterns.
ReGDiff couples regulated latent diffusion with repel-and-sink smoothing and short-range repulsion guidance to generate plausible, novel metamaterial voxel geometries, improving plausibility by 8.9%, novelty by 46.4%, and diversity by 128.6% over baselines.
Leviathan decouples input and output embeddings via learned continuous token vectorization, cutting perplexity up to 9% and rare-token perplexity by 81% with only 0.2% extra parameters.
CASPIAN detects cascade attacks in LLM multi-agent systems via online cross-channel causal monitoring, accurately identifying attack origins and propagation paths with sub-1% overhead.
SAGE mitigates long-horizon reasoning biases via symbolic closure analysis, using algebraic sparsification and hyperbolic guidance to suppress spurious branching and compounding errors, achieving up to 8-fold gains on sparse-reward benchmarks.