5 papers
Beyond Accuracy: Diagnosing Algebraic Reasoning Failures in LLMs Across Nine Complexity Dimensions
Parth Patil, Dhruv Kumar, Yash Sinha +1
Algebraic reasoning remains one of the most informative stress tests for large language models, yet current benchmarks provide no mechanism for attributing failure to a specific ca…
Beyond Local Code Optimization: Multi-Agent Reasoning for Software System Optimization
Huiyun Peng, Parth Vinod Patil, Antonio Zhong Qiu +2
Large language models and AI agents have recently shown promise in automating software performance optimization, but existing approaches predominantly rely on local, syntax-driven…
A Unit Proofing Framework for Code-level Verification: A Research Agenda
Paschal C. Amusuo, Parth V. Patil, Owen Cochell +2
Formal verification provides mathematical guarantees that a software is correct. Design-level verification tools ensure software specifications are correct, but they do not expose…
Do Unit Proofs Work? An Empirical Study of Compositional Bounded Model Checking for Memory Safety Verification
Paschal C. Amusuo, Owen Cochell, Taylor Le Lievre +3
Memory safety defects pose a major threat to software reliability, enabling cyberattacks, outages, and crashes. To mitigate these risks, organizations adopt Compositional Bounded M…
Recommending Pre-Trained Models for IoT Devices
Parth V. Patil, Wenxin Jiang, Huiyun Peng +7
The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like qua…