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arXiv AI By Yukuan Zhang, Mengxin Zheng, Qian Lou

MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation

Read the original on arXiv AI →

arXiv:2606. 11416v1 Announce Type: cross Abstract: Repository-level benchmarks for evaluating Large Language Model (LLM) code repair on Secure Multi-Party Computation (MPC) software do not yet exist, and directly transplanting general-purpose benchmarks such as SWE-bench fails on three structural fronts: (i) MPC repositories are dominated by generic Python infrastructure rather than cryptographic logic; (ii) high-value MPC fixes lack the standardized tests rigid extraction pipelines require; and (iii) standard fail-to-pass evaluation is insufficient for code that must also be cryptographically safe.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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