arXiv (OAI Expanded)
open access
Assisted Spatial Cognition Through Vision-Language Models
Multimodal AI, powered by Large Language Models (LLMs) and Vision-Language Models (VLMs), is transforming assistive technologies by enabling simultaneous processing of visual and textual data. This advancement holds significant promise for over 43 million visually impaired and neuro-divergent indivi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ThinkV2V: Unleashing the Reasoning Capability of MLLMs for Instruction-Guided Video Editing#1143142
- GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts#927168
- Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs#1061402
- Vision-Language Memory for Spatial Reasoning#998055
- The environmental sculpting of perception and action: Hierarchical inference dynamics drive environment-induced wellbeing#444306
Record · ID 1070218
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive