Skill Is Not Document: A Query-Conditional Benchmark and Two-Stage Retriever for LLM Agent Skill Routing
Paper • 2606.03565 • Published • 1
How to use tencent/R3-rerank-0.6b with sentence-transformers:
from sentence_transformers import CrossEncoder
model = CrossEncoder("tencent/R3-rerank-0.6b")
query = "Which planet is known as the Red Planet?"
passages = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]
scores = model.predict([(query, passage) for passage in passages])
print(scores)The latest agent skill reranking model at the 0.6B scale. R3-Reranker is the cross-encoder (rerank) stage of R3-Skill's two-stage retriever for query-conditional agent skill retrieval. It scores each (query, skill) pair jointly, paired with R3-Embedding-0.6B for recall.
from sentence_transformers import CrossEncoder
model = CrossEncoder("tencent/R3-rerank-0.6b")
query = "I need to compose music"
skills = [ # The format is "name | description | skill_md"
"music-composer | Composes original music | Creates music for various media formats ...",
"music-lyricist | Writes lyrics for songs | Creates lyrics for various music genres ...",
"music-editor | Edits and mixes music tracks | Provides audio editing and mixing services ...",
]
pairs = [(query, skill) for skill in skills]
scores = model.predict(pairs)
print(scores)
# [ 0.34937477 -1.7738094 -1.6604462 ]
@inproceedings{r3skill2026,
title = {Skill Is Not Document: A Query-Conditional Benchmark and Two-Stage Retriever for LLM Agent Skill Routing},
author = {Wang, Zifei and Wen, Wei and Ji, Qiang and Qiao, Ruizhi and Sun, Xing},
year = {2026},
url = {https://arxiv.org/abs/2606.03565},
}