How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code

3 months ago, we started a revival of Papers with Code (see also the announcement tweet ). Its goal is to make open AI research accessible and digestible, so that people can easily find the artifacts related to a paper, find state-of-the-art (SOTA) across the various domains of AI, share interesting research and build on top of each other's work. In other words, its goal is to power the wave of research that leads to the next Transformer .

Of course, making AI research accessible requires a powerful search engine, so that humans and agents can quickly find relevant and related work, either through the website or the pwc search CLI command , which agents can use via the Skill .

It's important to note that searching for research is not quite the same as searching for regular text. A useful paper search engine should find an exact title or arXiv identifier, but it should also understand a query such as “small language models for code generation” even when those words do not appear together in a paper. It needs to recognize that “the original BERT paper” is a navigational request, tolerate an incomplete title or typos, and still respond quickly when a model service is cold or temporarily unavailable.

Today, the system maintains embeddings for more than 110,000 current papers sourced from arXiv and Daily Papers . This post explains the architecture, the design decisions behind it, and the lessons we learned while taking it to production.

RRF is simple and robust, because it combines ranks rather than scores from two systems with different scales. Basically, if a paper is ranked high both by the lexical branch and the semantic branch, it has a higher chance of being ranked high by the hybrid search. We currently use equal branch weights and (k=60) (k is the "rank constant", a hyperparameter of the RRF algorithm).

We avoid this by treating the embedding format as a versioned API. Every paper is encoded as:

Corpus embedding and query embedding use the same model, but they are different infrastructure problems. Jobs optimize for throughput and bounded cost; Inference Endpoints optimize for availability and request latency.

For Papers with Code , we built this as a hybrid search system. This is also based on our prior experience at ML6 , where we developed RAG -based systems for clients. It turned out that hybrid search typically outperforms keyword- and vector-based search systems, as it combines the best of both worlds (see also this blog for more info). Keyword search finds exact mentions, whereas vector search finds more fuzzy, semantically similar terms. Note that rerankers (also called cross-encoders) can further improve the results, although they also come with additional overhead and latency.

Papers with Code relies on a PostgreSQL database, hence its full-text search capabilities provide a fast lexical baseline. For dense embeddings, pgvector is used to add semantic recall, and the reciprocal rank fusion (RRF) algorithm combines the two. Three Hugging Face services are used for the dense embeddings:

We deliberately split search into an offline corpus build and an online search service: