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Jiakun Li

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#artificial intelligence Preprint Aug 2026

Multilingual Safety Signals Are Multi-Layered: Filtering Safety-Degrading Data for Safer LLMs

MMSAFE is proposed, a multi-layer framework for multilingual safety-degrading data identification that captures both shared and language-specific safety signals and reduces the average harmful-response ratio compared with random filtering and achieves stronger average performance than the strongest single-layer baselin...

Jia-Kun Li, Guo-Wei Song, Si-Jia Li et al. · 0 citations
Preprint Aug 2026

TDD-Agent: Test-Driven Reasoning for Code Generation

TDD-Agent is introduced, which operationalizes the test-driven development paradigm for code generation and improves not only code correctness but also the effectiveness of the generated tests, yielding higher pass rates, coverage, and mutation scores, suggesting that tests can serve as evolving reasoning artifacts rat...

Hong Yu, Ke-Fan Li, Jia-Kun Li et al. · 2 citations

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