Generative Visual Code Mobile World Models
Paper
•
2602.01576
•
Published
•
41
Anthony W Figueroa
THEFIG
·
AI & ML interests
None yet
Recent Activity
upvoted
a
paper
10 days ago
Generative Visual Code Mobile World Models
upvoted
a
paper
10 days ago
Code2World: A GUI World Model via Renderable Code Generation
liked
a Space
about 2 months ago
Wan-AI/Wan2.2-Animate
Organizations
None yet
upvoted
2
papers
10 days ago
liked
a
Space
about 2 months ago
liked
2
Spaces
3 months ago
upvoted
a
paper
3 months ago
liked
a
Space
3 months ago
upvoted
an
article
7 months ago
Article
Building the Hugging Face MCP Server
- +2
•
67
liked
a
Space
10 months ago
liked
a
model
about 1 year ago
reacted to
singhsidhukuldeep's
post with 🚀
about 1 year ago
Post
3051
Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments.
Key innovations that set MiniRAG apart:
Semantic-aware Heterogeneous Graph Indexing
- Combines text chunks and named entities in a unified structure
- Reduces reliance on complex semantic understanding
- Creates rich semantic networks for precise information retrieval
Lightweight Topology-Enhanced Retrieval
- Leverages graph structures for efficient knowledge discovery
- Uses pattern matching and localized text processing
- Implements query-guided reasoning path discovery
Impressive Performance Metrics
- Achieves comparable results to LLM-based methods while using Small Language Models (SLMs)
- Requires only 25% of storage space compared to existing solutions
- Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%
The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.
This breakthrough opens new possibilities for:
- Edge device AI applications
- Privacy-sensitive implementations
- Real-time processing systems
- Resource-constrained environments
The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
Key innovations that set MiniRAG apart:
Semantic-aware Heterogeneous Graph Indexing
- Combines text chunks and named entities in a unified structure
- Reduces reliance on complex semantic understanding
- Creates rich semantic networks for precise information retrieval
Lightweight Topology-Enhanced Retrieval
- Leverages graph structures for efficient knowledge discovery
- Uses pattern matching and localized text processing
- Implements query-guided reasoning path discovery
Impressive Performance Metrics
- Achieves comparable results to LLM-based methods while using Small Language Models (SLMs)
- Requires only 25% of storage space compared to existing solutions
- Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%
The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.
This breakthrough opens new possibilities for:
- Edge device AI applications
- Privacy-sensitive implementations
- Real-time processing systems
- Resource-constrained environments
The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
- 1 reply
liked
a
Space
about 1 year ago
commented
a
paper
about 1 year ago
upvoted
4
papers
about 1 year ago
The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
Paper
•
2411.10323
•
Published
•
34
Large Language Model-Brained GUI Agents: A Survey
Paper
•
2411.18279
•
Published
•
30
Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction
Paper
•
2412.04454
•
Published
•
71
Ponder & Press: Advancing Visual GUI Agent towards General Computer Control
Paper
•
2412.01268
•
Published
•
1
liked
a
Space
about 1 year ago
upvoted
a
collection
about 1 year ago
liked
a
Space
about 1 year ago