From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search
- Published
- Source
- arXiv
- Paper number
- 743
- Field
- AI / General
- arXiv ID
- 2607.24280
Key points
- It uses commercial models only as offline teachers, so inference cost is zero.
- The structured JSON convention created by the multi-agent system links teacher and student.
- It strips away surface style to reduce stylistic contamination and hallucination.
- It achieves average success rates of 39.4% with Qwen3-1.7B and 44.4% with Qwen3-4B.
- Claude, GPT, and Gemini teachers are all within 2 points of each other, so API swapping is easy.
- Convention synthesis is a one-time cost of about $1,454.
Paper links
External research summaries. These are not HDATF publications or measured product results.