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{
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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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"arxiv_ids": [
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"2690.17728"
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