The Flow has not summarised this story yet — read it at arXiv Computation and Language.
MIDR (Multimodal Indexing for Document Retrieval) is a training‑free framework that enriches document indexes by converting rendered pages into verified textual fields with a multimodal LLM, then indexing those fields with BM25F and optionally fusing with dense retrieval. By shifting multimodal reasoning to index time, MIDR enables text‑centric serving while retaining multimodal evidence, achieving a 23.0% relative gain over BM25 on ViDoRe V3 and outperforming ColQwen2.5 on several domains with significantly smaller index memory and lower query latency.
arXiv:2606. 28344v1 Announce Type: cross Abstract: Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipelines that linearize HTML and discard layout, visual structure, and formatting.
arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.
The paper introduces TrioRAG, a graph-free multimodal retrieval-augmented generation framework that combines evidence from the question, an anchor image, and a VLM-enhanced query via late fusion. It also presents AutoQA, a benchmark featuring noisy web-sourced images that require reasoning across manuals. TrioRAG outperforms graph-based systems on three benchmarks while cutting costs and speeding up inference by 1.6–2.3×.
The paper presents a method for improving cross‑lingual retrieval in multilingual encoders by adding a query‑only low‑rank adapter and deterministic routing based on language. The adapter, trained against frozen document embeddings, is used for cross‑lingual queries while the original encoder handles same‑language queries, preserving existing performance. Using the SampoTron LoRA adapter with the Nemotron‑3‑Embed‑1B model, the authors report a 20.9% relative gain in nDCG@10 across six English, Finnish, and Swedish directions on a financial benchmark, with all cross‑lingual directions improving and same‑language performance maintained.