README.md

---

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