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Results show that CoSTAR enables small, locally deployable LLMs to achieve performance competitive with substantially larger LLMs for privacy-sensitive COBOL legacy systems.
Opaque industrial control binaries can now be traced back to their raw Structured Text source code with 95.89% top-1 accuracy across major PLC toolchains, breaking down a persistent barrier in industrial software auditing.
N2NMatcher achieves remarkable improvements in binary code similarity analysis by effectively countering the disruptive effects of function inlining.
Explicit semantic augmentation can boost LLM-based code translation success rates by over 200%, especially for smaller models.
