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It is argued that AI coding assistants should not only be evaluated by the code they generate, but also by how they mediate the transfer of that code into software artifacts, and soft barriers are proposed as one class of handoff-aware mechanisms.
This work introduces XL-CoGen, a three-stage multilingual code-generation pipeline that starts from a natural-language specification and a shared test list to generate correct implementations across multiple target languages and repairs the best candidate through diagnosis and minimal patching.
It is revealed that scaled encoders successfully absorb substantial representation load while still converging on the correct family of solutions, and this ability to adapt to varying surface forms is what truly matters for usability and generalizability in LLM and agentic applications.
The results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
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