LangMap introduces human-verified hierarchical open-vocabulary navigation benchmarks across scene, room, region, and instance levels with 18K tasks, and PlaNaVid achieves top RGB-only success via planning and memory.
ReFPO adds explicit reflow regularization to flow matching policy gradients, stabilizing training and enabling high-fidelity one-step inference that matches multi-step performance across control tasks.
DiffICL frames tabular synthesis as in-context learning using pretrained structural priors to avoid memorization, improving both quality and privacy in small-data settings.
Depth2Pose evaluates monocular depth estimators via downstream pose estimation accuracy using only camera poses, introducing a challenging out-of-distribution benchmark where standard metrics fail to predict generalization.
TreeGraft combines small and large drafters with a scheduler to build shared draft trees, boosting speculative decoding by 15.1% over single-drafter methods.
Platonic Representation Defense detects and purifies backdoored self-supervised encoder representations via cross-model energy functions without labels or training data. It substantially improves robustness across multiple encoders and over ten attacks in fully black-box settings.
A language-assisted clustering framework uses cross-modal relational signals and adaptive semantic centers to improve clustering accuracy by 2.6% over state-of-the-art methods.
GTP-FA decouples grasping from planning with failure attribution to diagnose failures and optimize both modules, substantially improving robotic manipulation success across diverse policy learners.
SoftGAC proposes a soft generative actor-critic with stochastic bridge policies that expose a tractable MaxEnt objective via single-pass sampling, outperforming diffusion and flow baselines on continuous control with lower latency.