Large language models (LLMs) have achieved impressive performance on text-based programming tasks, yet they remain unreliable for block-based languages such as Scratch. Scratch programs feature deeply nested, nonlinear structures, event-driven concurrency across multiple sprites, and tight coupling between code and mul...
Yuan Si, Si-Meng Han, Da-Ming Li et al.· Proceedings of the ACM on So...· 0 citations
This work presents file-backed weight adoption: a framework-independent producer maps each tensor with MAP_SHARED, wraps the pages as a no-copy GPU buffer, and exports a DLPack capsule that PyTorch or MLX imports as ordinary storage.
Yuan Si, Yufeng Lin, Da-Ming Li et al.· 1 citation
A user-centric framework for systematically auditing system prompts in AI systems, AISPA is introduced, a user-centric framework for systematically auditing system prompts in AI systems that examines specific parts of a system prompt and evaluates them along eight dimensions that matter to users.
Xiangning Lin, Shenzhe Zhu, Shu Yang et al.· arXiv.org· 0 citations
The resulting design principle is simple: in this regime, let the kernel own eviction, while model-specific knowledge is best spent on admission and advice.
Yuan Si, Yufeng Lin, Da-Ming Li et al.· 2 citations
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