In a blog post and on its Hugging Face model page on 6 November, Asteromorph presented three models that rerank search results. The company released downloadable weights only for AsteroRank-Lepton, which it describes as the lightest of the three. The stronger AsteroRank-Drachma and AsteroRank-Talent will become available through an API, the company said, and users can join a waiting list for them.

A reranker scores the documents found by an initial search and moves the most relevant to the top. Lepton gives each document a score between zero and one. The model is fine-tuned from Qwen3.5-4B. The company describes it as a 4-billion-parameter model, but Hugging Face lists its size as 5 billion parameters.

The description says what the search is for

Asteromorph calls its method context reranking. The search agent sends a short keyword query plus a one- to three-sentence description of the article it is looking for. The description names the system under study, the mechanism, the relevant conditions and the type of evidence needed. The company handles date and source restrictions with separate filters.

In the company’s example, the search is for para-selective borylation. If the description merely repeats the query, an article that reaches the same result by a different route contains almost all the same search terms and may score just as well.

Instruction-following rerankers can also take similar input. The difference lies in how the input is used, the company said: an instruction changes the task, whereas context states the purpose of the search. In Asteromorph’s own use, stating the search intent improved results more than phrasing the input as an instruction or a description. The company gives no figures for this.

The company’s own benchmarks

Asteromorph tested its models itself on public candidate lists. The metric was nDCG@10, which rewards placing relevant documents high in the top ten. The comparison models were zerank-2, Voyage rerank-3 and Voyage rerank-3-lite. In the BRIGHT and BEIR-Science benchmarks, the models were given no context.

On BRIGHT, Talent scored 32.30 and zerank-2, the strongest comparison model, 30.65. Drachma and Lepton also scored higher than zerank-2, the company said. On BEIR-Science, the scores of all six models differed by no more than 1.0 point.

The table shows the MAIR results. In MAIR, every query comes with a task instruction, which the company gave to AsteroRank as context. In this benchmark, Lepton finished behind Voyage’s lighter model. The company weighted the queries differently from the public evaluation program, so the averages do not match a direct rerun of the benchmark.

Source: Asteromorph blog. The company’s own measurement, 126 tasks from six domains.

In RCP-nDCG, a metric developed by Cohere, a large language model assesses the whole candidate set. On this metric, Talent scored 5.02 points and Drachma 1.67 points higher than Cohere’s Rerank 5 Pro on BRIGHT. Cohere published the Rerank 5 Pro figures itself, to one decimal place.

Licence allows only non-commercial use

Lepton is released under the CC BY-NC 4.0 licence, which does not allow commercial use. The model page has instructions for SGLang and vLLM, tested on a single Nvidia B200 GPU, as well as instructions for Sentence Transformers. The page warns that in SGLang version 0.5.20 the scheduler crashes if a reranking request lands in the same batch as, for example, a chat request; the bug was still unfixed in version 0.5.22. Users should therefore route only reranking requests to the server.