Internet-Draft Symbol Transport Protocol W. Franzin Intended status: Informational October 2025 Expires: April 2026 Date: 2025-10-15 Symbol Transport Protocol (STP) draft-franzin-stp-00 William Franzin Independent Technologist Abstract The Symbol Transport Protocol (STP) proposes a novel data representation and transport method that replaces raw byte sequences with symbol-based pattern acceleration. By identifying and transmitting recurring data structures as symbols instead of explicit bytes, STP seeks to reduce bandwidth, improve latency, and enhance efficiency across structured and semi-structured data domains. Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet-Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." Copyright Notice Copyright (c) 2025 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. 1. Introduction Modern data formats (e.g., JSON, XML, CSV, telemetry) contain high repetition and predictable structures. While compression algorithms like gzip or Brotli reduce redundancy, they do so reactively, requiring full decompression on every read. STP introduces an alternative: symbol transport, where a dynamic symbol dictionary evolves between sender and receiver, allowing shared context to represent recurring patterns compactly. This concept is inspired by cognitive compression - how the human brain forms and recalls symbols to represent patterns efficiently. Instead of bytes, STP transmits a symbolic abstraction layer, aligning with principles of efficient representation observed in human cognition. 2. Concept Overview STP defines a transport mechanism composed of the following elements: - Symbol Dictionary: A synchronized structure between peers mapping symbol identifiers to recurring patterns. - Symbol Frames: Packets of symbolic data, possibly referencing dictionary entries or introducing new patterns. - Reserved Symbols: Tokens for control flow (e.g., dictionary reset, out-of-band literal transmission). Example symbolic grid: SYM_A | SYM_B | SYM_C | NO_MATCH | CMD_SYNC | SYM_D | SYM_E | SYM_A | 3. Protocol Fit and Expected Performance Gains STP is most effective in domains with high structure reuse. Estimated bandwidth reductions include: - IoT Telemetry: 75% - Logging / Metrics: 70% - Web APIs: 60% - Web Headers: 55% - Database Replication: 45% - XML / SOAP Docs: 40% - Config Files: 35% - Chat / Text: 15% - Binary Streaming: <5% 4. Performance Comparison Symbol Transport achieves greater bandwidth efficiency and latency improvement than traditional compression in structured domains. Latency gains range from 65-70% in structured data to 3-10% in unstructured data. 5. Analysis Symbolic transmission benefits structured and semi-structured data where patterns repeat across sessions. Symbol reuse reduces payload size and eliminates decompression cycles. STP maintains persistent context and supports incremental updates. 6. Applications and Extensions 6.1 Communication and Networking - IoT telemetry, MQTT, Kafka, WebSocket - 40-75% bandwidth reduction 6.2 Storage and Databases - Write-ahead logs, Parquet/ORC - 30-60% traffic reduction 6.3 Cloud and Edge Computing - Serverless events, edge-core sync - Reduced cold-start latency 6.4 Machine Learning and AI Pipelines - Feature transport, symbolic reasoning - Up to 50% tensor reduction 6.5 Developer Tooling and Build Systems - Version diffs, CI/CD caching - Faster incremental builds 6.6 Games and Simulations - Multiplayer sync, procedural updates - 40-70% reduction in network updates 6.7 Knowledge Representation and Reasoning - RDF encoding, semantic web - 60-80% reduction in redundant data 6.8 Strategic Positioning and Integration Pathways 6.8.1 Open Standard Vision - Intended for IETF submission - No patents or proprietary lock-in - Reference implementations in C, Rust, Python 6.8.2 Legacy Compatibility - Wraps JSON, XML, and other formats - Compatible with Web APIs, message queues, databases 6.8.3 AI and Symbolic Synergy - Symbolic transport of embeddings and graphs - Supports hybrid neuro-symbolic architectures 7. Summary Table Domain Bandwidth Reduction Additional Benefits ------------------- ------------------- ------------------------- IoT / Telemetry 70-80% Lower latency, less CPU Databases 40-60% Less I/O, faster sync Cloud / Edge 50-70% Lower cost, faster start Machine Learning 30-50% Symbolic AI integration Tooling / Builds 20-40% Faster incremental builds Gaming / Simulation 40-70% Real-time responsiveness Knowledge Systems 60-80% Semantic-level efficiency 8. Future Work A minimal proof-of-concept could test STP using: - JSON telemetry streams - Web API exchanges - Log aggregation Metrics to collect: - Bandwidth savings - Round-trip latency - Symbol dictionary sync efficiency 9. Security Considerations STP introduces symbolic abstraction and persistent context. Implementers must ensure symbol dictionaries do not leak sensitive structure or metadata. Dictionary synchronization should be authenticated and integrity-protected to prevent injection or tampering. 10. IANA Considerations This document has no IANA actions. 11. Conclusion STP introduces a symbolic abstraction layer for machine communication, inspired by cognitive compression. It offers substantial efficiency gains in structured data systems and opens new possibilities for semantic, symbolic, and intelligent transport protocols. Author's Address William Joseph Franzin Independent Technologist Winnipeg, Manitoba, Canada Email: wfranzin@gmail.com