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The paper introduces a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, targeting both the understanding (SIQA-U) and scoring (SIQA-S) tracks of the SIQA challenge. It builds a multimodal index that merges textual semantics with fine‑grained visual features and employs a multi‑route retrieval and fusion mechanism to supply large language models with relevant reference cases, improving their evaluation of complex scientific images. The approach aligns well with human expert judgment and secured first place in the SIQA-U track at the ICME 2026 Grand Challenges.
Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers. However, existing image quality assessment (IQA) methods are predominantly designed for natural photographs or AI-generated content, which cannot be directly applied to scientific papers.
arXiv:2607. 27084v1 Announce Type: cross Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in scientific papers.
MiRAGE is a new evaluation framework for retrieval‑augmented generation (RAG) that handles multimodal sources such as audiovisual media. It uses a claim‑centric approach with two metrics: InfoF1, which measures factuality and information coverage, and CiteF1, which measures citation support and completeness. Human evaluation shows MiRAGE aligns well with extrinsic quality judgments, and an automatic implementation outperforms three text‑centric RAG metrics (ALCE, ARGUE, RAGAS) on text while uniquely generalizing to multimodal inputs.
arXiv:2606. 10194v1 Announce Type: cross Abstract: Climate change research increasingly requires AI systems that reason across text, dynamic visual content, and scientific figures, yet existing climate QA benchmarks are small, mostly textual, and cover a narrow range of models.
arXiv:2608. 14075v1 Announce Type: new Abstract: Scientific figures and tables encode essential experimental evidence, yet remain difficult for digital libraries and multimodal AI systems to retrieve and interpret.