Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose ShortageSim, which addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.

Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

State-of-the-art neural network verifiers demonstrate that applying the branch-and-bound (BaB) procedure with fast bounding techniques plays a key role in tackling many challenging verification properties. In this work, we introduce the \emph{linear constraint-driven clipping} framework, a class of scalable and efficient methods to enhance bound propagation verifiers. Under this framework, we develop two novel algorithms that efficiently utilize constraints to 1) reduce portions of the input space that are either verified or irrelevant to a subdomain in the context of branch-and-bound, and 2) directly improve intermediate bounds throughout the network. The process novelly uses linear constraints that are readily available during verification in a highly scalable manner compared to using off-the-shelf linear programming (LP) solvers. This reduction tightens bounds globally and can significantly reduce the number of subproblems handled during BaB. We show our clipping procedures can intuitively and efficiently be incorporated into BaB-based verifiers such as $\alpha, \beta$-CROWN, and is amenable to BaB procedures that split upon the input or activation space. We demonstrate the effectiveness of our procedure on a broad range of benchmarks where, in some instances, we witness a 96\% reduction in the number of subproblems during branch-and-bound, and also achieve state-of-the-art verified accuracy across multiple benchmarks.

Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advancements. However, GCP-CROWN currently relies on ${\it generic}$ cutting planes ("cuts") generated from external mixed integer programming (MIP) solvers. Due to the poor scalability of MIP solvers, large neural networks cannot benefit from these cutting planes. In this paper, we exploit the structure of the neural network verification problem to generate efficient and scalable cutting planes ${\it specific}$ to this problem setting. We propose a novel approach, Branch-and-bound Inferred Cuts with COnstraint Strengthening (BICCOS), that leverages the logical relationships of neurons within verified subproblems in the branch-and-bound search tree, and we introduce cuts that preclude these relationships in other subproblems. We develop a mechanism that assigns influence scores to neurons in each path to allow the strengthening of these cuts. Furthermore, we design a multi-tree search technique to identify more cuts, effectively narrowing the search space and accelerating the BaB algorithm. Our results demonstrate that BICCOS can generate hundreds of useful cuts during the branch-and-bound process and consistently increase the number of verifiable instances compared to other state-of-the-art neural network verifiers on a wide range of benchmarks, including large networks that previous cutting plane methods could not scale to.

Recently, many neural networks have been proposed for machine remaining useful life (RUL) prediction. However, most network architectures of the existing approaches are fixed. Since the sequential information depends on the input data and distributes differently, these fixed networks that cannot be dynamically adjusted according to the input data may not be able to capture this sequential information well, resulting in suboptimal performances. To mitigate this issue, we propose an adaptive and dynamical neural network (AdaNet), which can dynamically adjust its architecture according to the input data. A neural network is generally determined by kernel size, depth, and channel size. In this article, we aim to enable our proposed AdaNet to adjust its kernel size and channel size dynamically. First, we explore to adapt the deformable convolution to time-series data, which allows the convolutional kernel to change according to the feature map. With this deformable convolution, the convolutional kernels in the AdaNet become adjustable, which is beneficial to fully exploit the sequential information in time-series data, leading to accurate RUL prediction. In addition, a channel selection module is devised, which can selectively activate the feature channel according to the input, further improving the performance of our AdaNet. Extensive experiments have been carried out on the C-MAPSS dataset, demonstrating that our proposed AdaNet achieves state-of-the-art performances.