GRAPHLCP integrates graph topology and inter-node dependencies into localized conformal prediction via densification and PageRank-based structural proximity, improving conditional coverage efficiency on graphs.
Capacity-constrained online convex optimization with delayed feedback achieves near-standard regret with logarithmic tracking capacity via randomized scheduling and weighted FTRL.
CM2 replaces verifiable outcome rewards with checklist rewards for multi-turn tool-use RL, improving 8B models by 8, 12 points on agent benchmarks using simulated environments.
A reduction framework converts online convex optimization with delayed feedback into immediate feedback, improving delay-dependent regret bounds for both first-order and bandit settings via continuous-time decomposition.
Cat-DPO applies per-category adaptive safety margins to direct preference optimization, improving aggregate safety and reducing worst-category harm gaps across models.
A direct data-adaptive test for Gaussian graphical models in high-dimensional long-memory time series achieves asymptotic size and power consistency via block bootstrap.