Solver-Aligned Initialization Learning differentiates through SCF solvers to train transferable ML initial guesses, reducing iterations by up to 37% on molecules up to 10× larger than training data.
Multi-variable conformal prediction extends calibration to vector-valued scores with multiple variables, removing data splitting while preserving coverage and yielding smaller, more stable prediction sets.
A multi-task virtual sensor model predicts diverse targets via shared representations, reducing computation up to 415x and memory 951x while improving accuracy over isolated and foundation alternatives.
Mean-field transformers exhibit rapid token distribution concentration onto projection-driven limits with explicit Wasserstein bounds scaling in inverse temperature β and time t.
FineVision unifies 24 million vision-language samples via rigorous curation and decontamination, and models trained on it outperform existing open mixtures across broad evaluations.
Geospatial foundation model literature lacks standardized evaluation protocols, causing widespread cross-paper scoring discrepancies and unreleased weights, so six concrete community standards are proposed.
SILO uses hierarchical self-improvement imitation with biologically guided stochastic beam search to optimize protein fitness under tight oracle budgets, outperforming baselines across eight landscapes.
A neuro-symbolic approach couples large reasoning models with model checkers to iteratively repair synthesized Verilog via sound symbolic feedback, solving more benchmarks than dedicated synthesis tools and enabling natural-language specification autoformalization.
d-OPSD applies on-policy self-distillation to diffusion LLMs via suffix conditioning and step-level supervision, cutting optimization steps by ~90% versus RLVR while outperforming baselines on reasoning benchmarks.
Joint Self-Improvement uses a joint generative-predictive model and self-improving sampling to reduce distribution shift and efficiently generate optimized molecules under limited evaluation budgets.
DiffScore evaluates text with masked diffusion models using bidirectional context to eliminate positional bias and decompose quality into fluency and faithfulness, outperforming autoregressive baselines.
DinoComplete distills DINO semantic priors into voxel features and fuses them via multi-scale voxel state-space modeling to achieve efficient, robust 3D shape completion on unseen categories and noisy scans.
Prompt2Seg conditions frozen diffusion segmentation models on spatial prompts for zero-shot interactive instance segmentation across diverse visual domains.
DriftBench finds iterative LLM ideation increases complexity and reduces constraint adherence, with models often violating rules they accurately recall and judges under-detecting violations.
A mean-pool DeepSet trained on pairs learns set encoders that generalize to arbitrary sizes, letting inference heads scale to thousands of observations with minimal compute.