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| 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 | |