Code correctness should be checked automatically with the CI and testsuite. New tests should be added. This is exactly what makes sure these stupid errors don't bother the reviewer. Same for the code formatting and documentation.
Then you can build a true hierarchy of citation dependencies, checked 'statically', and have better indications of impact if a fundamental truth is disproven, ...
Could you provide a proof of concept paper for that sort of thing? Not a toy example, an actual example, derived from messy real-world data, in a non-trivial[1] field?
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[1] Any field is non-trivial when you get deep enough into it.
totally agree with your thinking here, we can't just give this to an LLM, because of the need to have industry-specific standards for what is a hallucination / match, and how to do the search
One could submit their bibtex files and expect bibtex citations to be verifiable using a low level checker.
Worst case scenario if your bibtex citation was a variant of one in the checker database you'd be asked to correct it to match the canonical version.
However, as others here have stated, hallucinated "citations" are actually the lesser problem. Citing irrelevant papers based on a fly-by reference is a much harder problem; this was present even before LLMs, but this has now become far worse with LLMs.
the tool gptzero used in the article also detects if the citation supports the claim too, if you scroll to "cited information accuracy" here: https://app.gptzero.me/documents/1641652a-c598-453f-9c94-e0b...
this is still in beta because its a much harder problem for sure, since its hard to determine if a 40 page paper supports a claims (if the paper claims X is computationally intractable, does that mean algorithms to compute approximate X are slow?)