Wenlin Lin

Open-domain multimodal document QA requires first locating the right passages and then reading them, yet current retrieval-augmented agents give one model both kinds of tools and let it schedule itself. KnowForge separates the pipeline by job: a search-only Locator agent, a code-scheduled bridge judge that forces the missing second hop, a bounded evidence pool merging every search ranking, and a Deep Reader that only reads text, images and tables. With a self-hosted Qwen3.6-35B-A3B, it reaches F1 47.5 on the full M3DocVQA (2,441 questions, 3,368 PDFs), 11.0 above the published M3DocRAG and 7.1 above one-shot retrieve-and-answer on the same index and model.

Open-domain multimodal document QA requires first locating the right passages and then reading them, yet current retrieval-augmented agents give one model both kinds of tools and let it schedule itself. KnowForge separates the pipeline by job: a search-only Locator agent, a code-scheduled bridge judge that forces the missing second hop, a bounded evidence pool merging every search ranking, and a Deep Reader that only reads text, images and tables. With a self-hosted Qwen3.6-35B-A3B, it reaches F1 47.5 on the full M3DocVQA (2,441 questions, 3,368 PDFs), 11.0 above the published M3DocRAG and 7.1 above one-shot retrieve-and-answer on the same index and model.