3 papers
cs.CL2026
Multilingual Safety Signals Are Multi-Layered: Filtering Safety-Degrading Data for Safer LLMs
Jiakun Li, Guowei Song, Sijia Li +3
Preserving safety alignment during large language models fine-tuning is critical, however, recent studies have demonstrated that even benign fine-tuning data may contain safety-deg…
cs.SE2026
TDD-Agent: Test-Driven Reasoning for Code Generation
Hongyue Yu, Kefan Li, Jiakun Li +4
Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approac…
cs.AI2026
Efficient Test-Time Scaling via Temporal Reasoning Aggregation
Jiakun Li, Xingwei He, Kefan Li +3
Test-time scaling improves the reasoning performance of large language models but often results in token-inefficient overthinking, where models continue reasoning beyond what is ne…