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  "paper": {
    "title": "Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination",
    "authors": [
      "Anthony Maio"
    ],
    "abstract": "As multi-agent LLM systems scale,coordination bandwidthbecomes a primary cost\ndriver: every token spent on routing, intent framing, and redundant context is paid repeat-\nedly across agents and turns. Current approaches waste 40–60% of compute on coordination\noverhead, with communication costs scalingO(n2)as agent counts increase.\nThis paper introducesSlipstream, a protocol that performssemantic quantization:\nmapping free-form messages onto a sharedUniversal Concept Reference (UCR)and\ntransmitting compactmnemonic anchorsthat identify structured intents. Unlike syn-\ntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits\nnatural-language mnemonics that tokenize efficiently across model architectures.\nSlipstream combines (1) a symbolic4D semantic manifold—Action, Polarity, Domain,\nUrgency—with (2) a data-drivenvector engine(embeddings + nearest-centroid retrieval)\nplus anevolutionary extension layerthat learns new anchors from low-confidence traf-\nfic. Results show82% token reduction(41.9→7.4 tokens average) while maintaining\nsemantic fidelity, making large-scale multi-agent deployments economically viable."
  },
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    "urls": [
      "https://github.com/anthony-maio/slipcore",
      "https://modelcontextprotocol.io/,",
      "https://www.linuxfoundation."
    ],
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    "arxiv_ids": [
      "1982.10564",
      "2690.17728"
    ],
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      "10.1109/TIT.1982.1056489",
      "10.1145/1772690.1772862",
      "10.18653/v1/D19-1410"
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    "space_title": "Slipstream: Semantic Quantization for Efficient Multi-Agent Coordination",
    "tags": [
      "semantic-quantization",
      "multi-agent-systems",
      "protocol-standards",
      "token-ef-"
    ],
    "emoji": "📄",
    "colorFrom": "blue",
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    "chunks_jsonl": "paper_chunks.jsonl",
    "rendered_pages": [
      {
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        "path": "pages\\page_01.png"
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