arXiv AI
HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering
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GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG
arXiv:2608. 15056v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation.
arXiv AI
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SAFE-G: Structure-aware Faithful Evidence-guided Generation for Knowledge-based Visual Question Answering
arXiv:2608.21796v1 Announce Type: cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) aims to answer queries that necessitate reasoning over external knowledge sources beyond the visua...
arXiv AI
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Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
arXiv:2607. 22643v1 Announce Type: new Abstract: Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal input over a flat evidence space.
arXiv AI
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DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents
DocHop is a new benchmark that tests multimodal large language models on integrated chart‑context reasoning within document‑style images. The benchmark presents narrative text that imposes multi‑step compositional constraints, while charts supply the data needed to answer questions grounded in semantic reference labels. It contains 2,074 examples across six task categories, generated via a stochastic logic‑first pipeline that controls reasoning depth and visual density, and shows a large performance gap between humans (over 90% accuracy) and the best models (62.83%).
arXiv Computer Vision
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Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning
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arXiv AI
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Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering
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