MicroWorld：用多模态属性图弥合 MLLM 的微观领域差距

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Abstract:Multimodal large language models (MLLMs) show remarkable potential for scientific reasoning, yet their performance in specialized domains such as microscopy remains limited by the scarcity of domain-specific training data and the difficulty of encoding fine-grained expert knowledge into model parameters. To bridge the gap, we introduce MicroWorld, a framework for structured-knowledge augmentation with a complementary visual retrieval pathway, using a multimodal attributed property graph (MAPG) without domain-specific fine-tuning. MicroWorld extracts biomedical entities and relations via scispaCy or LLM-based triplet mining, aligns images and entities in a shared embedding space using Qwen3-VL-Embedding, and assembles a knowledge graph comprising approximately 106K nodes and 2.62M typed edges spanning eight relation categories. At inference time, a graph-augmented retrieval pipeline matches query entities to the MAPG and injects structured knowledge context into the MLLM prompt. On MicroVQA, MicroWorld raises Qwen3-VL-8B-Instruct accuracy from 48.8% to 67.1% (+18.3 percentage points; 37.5% relative), exceeding zero-shot GPT-5 (59.4%). The identical retrieved context raises GPT-5 to 71.5%, and GPT-5's own entity extraction reaches 73.3%. On MicroBench, 8B Single-Pass improves overall accuracy by 4.0 percentage points. The benefits are greatest for knowledge-intensive reasoning; fine-grained visual discrimination and the additional visual-pathway contribution remain limited. Code and accompanying resources: this https URL.

Submission history

From: Manyu Li [view email]
[v1] Mon, 11 May 2026 07:35:55 UTC (1,409 KB)
[v2] Sun, 25 Oct 2026 09:12:01 UTC (2,756 KB)