arXiv AI
Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs
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When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning
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VisKG-LM: Compiling Knowledge Graphs into Visual Memory for Multiple-Choice Question Answering
VisKG‑LM proposes compiling retrieved knowledge graph subgraphs into static visual memories rather than re‑encoding them during each inference step. The method serializes each subgraph as Relation‑Labeled Paths, renders them as images that preserve the graph’s branching structure, and caches these images for reuse. At inference, a language model processes the question and candidate text first, then consults the cached visual memory only at its final layer, yielding improved performance on CommonsenseQA, OpenBookQA, and MedQA‑USMLE compared to both text‑only baselines and a large vision‑language model.