Papers with MBA
NeuReduce: Reducing Mixed Boolean-Arithmetic Expressions by Recurrent Neural Network (2020.findings-emnlp)
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| Challenge: | Mixed Boolean-Arithmetic (MBA) expressions are difficult to simplify because of interleaving bitwise and arithmical operations. |
| Approach: | They propose a method to learn and reduce MBA expressions using a string to string method . they propose to use a dataset to train the method to reduce MBA rules . |
| Outcome: | The proposed method outperforms all other tools in terms of accuracy, solving time, and performance overhead. |
ACE: A LLM-based Negotiation Coaching System (2024.emnlp-main)
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| Challenge: | The rapid progress of LLMs has led to the development of more sophisticated AI tutoring systems. |
| Approach: | They develop an LLM-based assistant for coaching negotiation that provides users with targeted feedback for improvement. |
| Outcome: | The proposed system improves negotiation performance significantly compared to a system that doesn’t provide feedback and one which uses an alternative method. |
gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation Using Transformer Architectures (2025.findings-acl)
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| Challenge: | Mixed Boolean-Arithmetic (MBA) obfuscation protects intellectual property by converting programs into complex forms that are difficult to analyze. |
| Approach: | They propose a mixed-boolean-arithmetic (MBA) obfuscation framework that transforms a Transformer-based neural encoder-decoder into a truth table that is an automatically constructed semantic representation of an expression's behavior. |
| Outcome: | The proposed framework improves performance and highlights the importance of internal semantic expressions in recovering obfuscated code to its original form. |