README.md

metadata

title: 'Slipstream: Semantic Quantization for Multi-Agent Coordination'
emoji: 📄
colorFrom: blue
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: false
license: mit
tags:
  - semantic-quantization
  - multi-agent-systems
  - protocol-standards
  - token-efficiency

Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination

This Space was generated from a research paper PDF.

What you can do here

• Live Quantizer: Type messy natural language and watch it get quantized to a UCR anchor (the core demo!)
• Start here: guided entry points (summary / limitations / thread)
• Gallery: extracted figures or page previews
• Chat: ask questions about the paper
• Share Kit: generate a tweet thread / talk outline / FAQ
• Model Playground: chat with a referenced HF model (requires HF_TOKEN)

Optional secrets

If you add these as Space secrets, Chat + Share Kit become generative:

• HF_TOKEN: Hugging Face token (read access is sufficient for inference; write is not needed at runtime)
• PAPER_LLM_MODEL: e.g. meta-llama/Meta-Llama-3-8B-Instruct (or any chat-completion capable model)

Build provenance

• Source PDF: slipstream-paper.pdf
• Extracted pages: 7