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300M scientific papers. Almost none are structured for machines to reason over. @PKoellinger + @nashgambit of @DeSciClaims show how miners can extract claims + evidence, validators verify them, and incentives turn the literature into trusted, AI-ready data. Catch them at Exploit:

Dozens of vector quantization papers come out every year. When we surveyed them, we noticed that most quantizers are not new algorithms. They are the same handful of primitive operations, strung together in a different order. So we built VQ-bench: an open-source library of those primitives, composable into pipelines. E-RaBitQ, one of the strongest methods we tested, is four primitives in a list. Swap one and you have a new quantizer, evaluated exactly the way every other method is evaluated. We used it to benchmark 14 popular quantizers on recall, reconstruction error, and encode time. Two takeaways so far: PQ and OPQ have the lowest reconstruction error, and EDEN matches E-RaBitQ on recall while encoding much faster. This is a first iteration. We want your feedback, corrections, and contributions, and we will keep adding quantizers over time. Website: Blog: Repo: Paper (VecDB@VLDB 2026):