5 papers
ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing
Dong Chen, Chih-Duo Hong, Fang Yu
Concolic testing combines concrete execution with symbolic constraint solving, but Python programs expose recurring limits. Library calls can cause symbolic variables to downgrade…
Signature filtering: a lightweight enhancement for statistical watermark detection in large language models
Chih-Duo Hong, Yen-Pang Chen, Fang Yu
Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive,…
Influence-Guided Concolic Testing of Transformer Robustness
Chih-Duo Hong, Chih-Cheng Yang, Yu Wang +1
Concolic testing for neural networks alternates concrete execution with constraint solving to search for inputs that flip model decisions. We present a concolic tester for Transfor…
Robustness Verification of Recurrent Neural Networks with Abstraction Refinement
Li-Jen Lin, Chih-Duo Hong
Certified local robustness verification for recurrent neural networks (RNNs) is challenging because approximation errors introduced by nonlinear relaxations can propagate through r…
Concolic Testing on Individual Fairness of Neural Network Models
Ming-I Huang, Chih-Duo Hong, Fang Yu
This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the concolic testing tool PyCT, we gen…