arXiv Computer Vision By Sunwoo Kim

Wontopos Tablet 2: Measuring Multilingual and Multimodal Memory Retrieval Without Lexical Matching

Read the original on arXiv Computer Vision →

The paper evaluates the tablet‑2 long‑term memory engine on multilingual text benchmarks and cross‑lingual photo retrieval without lexical matching. Tablet‑2 achieves high accuracy on LongMemEval‑S (95.7%) and moderate accuracy on BEAM‑1M (67.5%), with minimal variance across runs. In multimodal tests, it outperforms BM25 on image‑cell recall and shows significant language‑dependent performance gaps, especially for low‑resource languages.

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arXiv AI
Sep 2

MIDR: Enrichment-Augmented Indexing for Multimodal Document Retrieval

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.

By Debanjan Mahata, Atharva Tendle, Daniel Preotiuc-Pietro, Yong Zhuang, Ozan Irsoy