A time-sensitive testing-by-betting framework favors early rejection via time-weighted rewards, yielding Bellman-optimal e-processes and an exponential-decay-optimal criterion recovering classical growth-rate optimality at large scales.
Language generation in the limit is recast as recall-precision trade-offs, showing that allowing infinitely many vanishing-frequency hallucinations can strictly increase recall when adversaries withhold target portions.
PGLD optimizes synthesis-aware stochastic DNA libraries via policy gradients to bypass synthesis cost limits, enabling million-sequence libraries for antibody exploration at low cost.
Targeted Full Conformal Prediction uses vision-language models to prune labels and scale full conformal image classification with stable coverage and modest overhead.
Linear probes on LLM residual streams identify a shared preference vector tracking pairwise choices across personas, with cross-persona transfer and causal steering.
Multiple grids per group improve 4-bit quantization by selecting better grids per group, consistently boosting accuracy over single-grid FP4 for weights and activations.
CLoSeR detects loop candidates via global descriptors to close loops in streaming reconstruction, reducing drift and producing consistent kilometer-scale geometry with SE(3) pose optimization.
GenRec separates reconstruction and generation via observation masks to preserve fidelity in visible regions while synthesizing plausible unobserved content.
Benchmarking thirty metrics on ten simulated complex systems shows only causal metrics reliably validate high-level explanations when testing unmapped-variable faithfulness, leading to the Causal Abstraction Error metric converging with thirty interventions.
Prediction-intervention games model leaders choosing predictors against followers intervening on covariates; stable-blanket predictors are provably optimal or near-optimal.
Divide et Calibra uses vector quantization to learn shared, region-specific multiclass calibration maps that improve local calibration without reducing latent dimensions.
Under local PŁ conditions, unique optimistic lower-level selection ensures hyper-gradient differentiability via pseudoinverses, yielding HG-MS with manifold-dependent convergence and strong LLM reweighting results.
Morph is a flexible-size generative model for 3D molecular design that uses unbalanced optimal transport to dynamically adapt molecular size, improving property steering and enabling out-of-distribution generation.