jsonplus cannot deserialize generic pydantic(v2) types correctly

Finding: In an external report (not reproduced by KFA), JsonPlusSerializer in langgraph-checkpoint returns a plain dict instead of the model instance when round-tripping a parametrized pydantic v2 generic such as MyGeneric[MyModel]. The report was made with pydantic 2.11.7, langchain_core 0.3.68 and Python 3.13.5, and a later contributor reports the same on main commit 7daa3ab [issue](https://github.com/langchain-ai/langgraph/issues/6102). Symptom: the reporter's test calls dumps_typed (msgpack) and then loads_typed. The result compares equal to {'inner': {'hello': 'hello'}} rather than to the original instance. No exception is raised [issue](https://github.com/langchain-ai/langgraph/issues/6102). Condition: the reporter says it works only when the specialized type is also defined as a module-level name (MyNewModel = MyGeneric[MyModel]) [issue](https://github.com/langchain-ai/langgraph/issues/6102). Contributor analysis (hypothesis, not maintainer-confirmed): the serializer records the class name as 'Box[Inner]', and the decoder looks that name up on the module, so the lookup fails when no such attribute exists. The same commenter reports a warning but no error, and that in strict mode (LANGGRAPH_STRICT_MSGPACK=true) allowlisting the origin, the argument, or the mangled name still yields a dict [comment](https://github.com/langchain-ai/langgraph/issues/6102#issuecomment-5847016819). Fix status: not fixed in any source supplied. Commenters describe PRs #9096 and #9203 that serialize the generic origin plus type args and check the allowlist on all of them. They say legacy payloads still load and that both PRs were auto-closed by an assignment-gate workflow [comment](https://github.com/langchain-ai/langgraph/issues/6102#issuecomment-5925420282) [comment](https://github.com/langchain-ai/langgraph/issues/6102#issuecomment-5990543965). No merge, fix commit or release is evidenced. The issue is open, and no maintainer has responded in the supplied comments. Limitations: all evidence is external reports from commenters with no maintainer association. KFA did not reproduce the problem or run the PR tests. The affected released version is unknown, and the comment timeline may omit middle comments. Actionable diagnosis if you hit this: check whether your state contains a parametrized pydantic generic created inside a function or in another module, and whether you got a dict back from a checkpoint. The reporter's module-level alias is the only workaround, and it is unverified by KFA.