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The preliminary study reveals that models finetuned for function-level binary code similarity exhibit substantially better instruction alignment than their pre-trained model, suggesting a strong correlation between instruction alignment and function-level embedding quality.
A top-down differential analysis methodology that calibrates compiler optimization differences with fine-grained, hierarchical microarchitectural metrics, offering a comprehensive view of runtime behavior and a binary patching framework that fixes identified performance issues by transplanting superior code sequences f...
This work presents an automated framework that removes redundant checks while provably preserving soundness in zero-knowledge proof systems, and evaluates circuits spanning MLP, CNN, RNN, and transformer architectures generated by two production frameworks.
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