This paper studies the behavior of mixture-of-experts (MoE) models under distribution shift and proposes an adversarial reweighting method to improve their calibration.

The paper evaluates multi-agent LLM oracle systems for prediction market resolution, finding that independent aggregation with confidence-weighted voting significantly outperforms single-model baselin…

The paper proposes DAG-MoE, a novel sparse Mixture-of-Experts framework that replaces standard weighted-sum aggregation with structural aggregation to enhance model performance and enable multi-step r…

The paper introduces an LLM-agent framework to solve the 'last-mile forecasting' problem, bridging the gap between raw statistical predictions and business-ready forecasts by incorporating weakly stru…

The paper proposes Under-Cali, an uncertainty-driven dual-expert calibration framework, to achieve stable and efficient online forecasting for irregularly sampled multivariate time series.

This paper introduces an activation hierarchy and proves a Predictor-Impossibility Theorem, showing that no effective predictor family can determine all stage languages. It also establishes a slice th…

This paper proposes a new training objective for sample-based generative models that considers decision maker's cost structure.

The paper proposes an objective-wise reputation-market mechanism to dynamically calibrate and gate LLM-generated expert priors in multi-objective Bayesian optimization, showing that dynamic calibratio…

The paper proposes K-line--Quantile Sequential Projection (KQSP), a method to reconcile quantile and K-line crossing inconsistencies in probabilistic K-line forecasting without reordering, specialized…

The paper introduces a unified theoretical framework for gradient aggregation in multi-objective optimization, establishing convergence rates and sufficient conditions for achieving Pareto stationarit…

This paper shows that stochastic nonconvex optimization can be reduced to ordinary static regret minimization in online convex optimization, and establishes convergence rates for smooth and Lipschitz…

The paper introduces ForeSci, a novel benchmark that evaluates LLM agents' ability to make forward-looking research judgments using only historical evidence, finding that explicit evidence organizatio…

ShaplEIG introduces a Bayesian experimental design framework to efficiently and adaptively estimate Shapley values by minimizing the number of required costly function evaluations.

This paper accelerates conformal prediction by incorporating approximate leave-one-out estimators and establishes asymptotic coverage and efficiency.