2 papers
cs.CV2026
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
Jiahui Guang, Zexun Zhan, Zhenlin Xu +5
Multimodal Large Language Models (MLLMs) may memorize sensitive cross-modal information during pretraining. However, existing MLLM unlearning benchmarks rely on synthetic knowledge…
cs.SE2026
SR-Eval: Evaluating LLMs on Code Generation under Stepwise Requirement Refinement
Zexun Zhan, Shuzheng Gao, Ruida Hu +1
Large language models (LLMs) have achieved remarkable progress in code generation. However, existing benchmarks mainly formalize the task as a static, single-turn problem, overlook…