This is a reranker fine-tuned for AI agent skill retrieval. Given a natural-language user request and a candidate agent skill, it scores how relevant and useful the skill is for the request. It is designed as the second stage after a first-stage retriever such as SkillRet-Embedding-0.6B or SkillRet-Embedding-8B. The model is fine-tuned from Qwen/Qwen3-Reranker-0.6B on the SkillRet benchmark training split with binary cross-entropy on the yes/no token probability. It keeps the scoring interface of Qwen3-Reranker. Technical report: SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents (arXiv:2605.05726) Each skill document is body, the same representation used by the SkillRet…
Open weights
apache-2.0
596M parameters
40,960 tokens
transformers
Enterprise policy decision model — Korean-supervised, zero-shot English. The boundary-targeted checkpoint from When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned? Merged full weights — not an adapter — with the pointer head shipped alongside. It decides which one of seven actions an enterprise agent should take under a written policy, before any text is generated: trained on contrastive boundary pairs — records differing in one decision-relevant factor across three boundaries (answer vs. call a tool, call a tool vs. ask for confirmation, missing information) — against a control given the same number of tokens of randomly sampled in-domain data. Data was the…
Open weights
apache-2.0
27.8B parameters
262,144 tokens
transformers