#MultimodalSearch

PPC Landppcland
2025-06-25

ICYMI: Google launches AI Mode in India with multimodal search capabilities: Advanced reasoning and visual search features arrive as experimental Labs feature following global expansion strategy. ppc.land/google-launches-ai-mo

PPC Landppcland
2025-06-24

Google launches AI Mode in India with multimodal search capabilities: Advanced reasoning and visual search features arrive as experimental Labs feature following global expansion strategy. ppc.land/google-launches-ai-mo

PPC Landppcland
2025-04-16

ICYMI: Google expands multimodal capabilities in search with AI Mode rollout: Google's AI Mode brings advanced search features using Gemini 2.0 technology for complex queries. ppc.land/google-expands-multim

2023-09-26

semion.io is an interesting multimodal search engine that retrieves academic articles from arxiv and displays side by side both an abstract of the article and figures from the article. It does allow you to do article search by text likely to occur in the illustrations/tables (or its caption), as each article is indexed in a directed graph with nodes representing each article component such individual figures, tables, equations, etc.

semion.io

#search #multimodalSearch

semion.io dissects each manuscript into constituent components (paragraphs, tables, figures, chapters, equations...) and determines links, interconnections and citations.

Internally, the semion search engine constructs a single directed graph of the combined information contained in all preprints on arXiv — the paper graph.

Each node represents a figure, paragraph, equation or other constituent component and its associated description. Graph edges encode their interconnections, both within each original document and across paper boundaries.

The task of retrieving results for a given search query thus maps onto a large-scale graph search problem.

Under the hood, semion.io determines relevant partitions of the paper graph for each search request, then traverses the sub graph and computes a ranking of nodes that are deemed most relevant when placed in proper context.

Client Info

Server: https://mastodon.social
Version: 2025.04
Repository: https://github.com/cyevgeniy/lmst