Streamlined Interagent Protocol (Slipstream): Semantic Quantization for Efficient Multi-Agent Coordination

As multi-agent LLM systems scale, coordination bandwidth becomes a primary cost driver: every token spent on routing, intent framing, and redundant context is paid repeatedly across agents and turns. Current approaches waste 40–60% of compute on coordination overhead, with communication costs scaling O(n2) as agent counts increase.

This paper introduces Slipstream v3, a protocol that performs semantic quantization by mapping free-form messages onto a factorized Force-Object intent model. Unlike Slipstream v2, which used 46 flat mnemonics (a hard 46-way classification problem for small models), v3 splits intents into two orthogonal dimensions: Force (12 closed tokens describing speech acts) and Object (31+ extensible tokens describing domain concepts).

This reduces the classification difficulty to 12-way + 31-way while maintaining the same semantic expressiveness. Unlike syntactic compression (which fails due to BPE tokenizer fragmentation), Slipstream transmits natural-language mnemonics that tokenize efficiently across model architectures. The system combines (1) a symbolic 4D semantic manifold—Action, Polarity, Domain, Urgency—with (2) a pointer-based fallback mechanism for unquantizable content. Results show 82% token reduction (41.9 → 7.4 tokens average) while maintaining semantic fidelity. 

The v3 implementation includes 506 conformance tests, zero core dependencies, and is published on PyPI. This makes large-scale multi-agent deployments economically viable while enabling small models (<10B parameters) to reliably learn the protocol.

Keywords: Semantic Quantization, Factorized Intent Models, Multi-Agent Systems, Protocol Standards, Token Efficiency, Agentic AI