Policy-gradient dynamics with partner selection are solved analytically, proving population variance is necessary for cooperation and deriving conditions for a stationary cooperative distribution.
SCDBench benchmarks LLM smart-contract decompilers on 600 real contracts via semantic replay, finding even top models perfectly recover only 42 and same-model repair substantially helps.
SKMD introduces symmetry-aware interacting-particle dynamics for active MLIP learning that preserves Boltzmann sampling, yielding faster convergence with fewer training iterations.
The Nanotechnology Molecular Optimization benchmark replaces proxy drug metrics with quantum simulations for nanomaterials, showing simple methods outperform advanced ones and revealing new structural motifs.
EoupCT estimates unknown pre-training gradients via learnable pseudo-data prompts and orthogonalizes updates to preserve general knowledge during continual LLM fine-tuning.
Multivariate Kernel Score yields geometry-adapted conformal regions via anisotropic MMD, guaranteeing finite-sample coverage with dimension-free rates and smaller volumes than ellipsoidal baselines.