TriSearch uses reinforcement learning and circuit-based flip representations to optimize triangulations across dimensions, discovering more Calabi-Yau triangulations than existing samplers.
A training-free test-time defense uses stochastic resonance of latent ensembles via input translations to recover up to 68.1% of adversarial accuracy loss on classification and dense prediction tasks.
MURPHY extends GRPO to multi-turn code generation via feedback-conditioned rollout trees with retrospective credit assignment, achieving up to 6% absolute pass@1 gains over prior methods.
PG-LRF uses a physiology-guided latent rectified flow with an electro-hemodynamic simulator to generate physiologically plausible ECGs from PPG, improving generation and cardiovascular disease classification.
kFFM replaces arbitrary pairing in Functional Flow Matching with kernel optimal transport to improve infinite-dimensional generative modeling and outperforms baselines on time-series and PDE benchmarks.