Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers
Owen CoxApril XuWeiyu Xu
Aug 2026
Artificial IntelligenceCybersecurity
Abstract
In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.
Investigating how experienced developers use agents in building software, including their motivations, strategies, task suitability, and sentiments finds that while experienced developers value agents as a productivity boost, they retain their agency in software design and implementation out of insistence on fundamental software quality attributes.
An adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs is developed that combines exploration and exploitation to improve the surrogate model accuracy with the fewest possible runs of the expensive physics-based model.
B. Kapusuzoglu, S. Mahadevan, Shunsaku Matsumoto et al.· Structural And Multidiscipli...· 17 citations
An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.
Linghan Huang, Bo Li, Huaming Chen et al.· 12 citations· ⚡2
This review provides a systematic literature review of LLM-based Verilog code generation, analyzing 102 papers (70 published and 32 high-quality preprints) from SE, AI, and EDA venues and outlines a roadmap highlighting potential opportunities in LLM-assisted hardware design.
This work introduces Behavior-Outcome Freedom (F), a pre-synthesis diagnostic of signed behavior-outcome rank mismatch, and formalizes its candidate-conditional role through Signed Anchor-Rank Transfer, which preserves validated capability resources, removes runtime orchestration, and conditionally inherits pipeline guidance using a calibrated rule over F.
Binyan Xu, Dong Fang, Haitao Li et al.· arXiv.org· 10 citations
Simulation results confirm the effectiveness and benefits of DMs in generating neighbor velocity estimates in a four-UAV swarm coordination task using Deep Reinforcement Learning (DRL), and explore the integration of DMs with RL and DT.
