TASS (Tokeniser-Aware Structured Shorthand) is a compact, human-readable text serialisation format using the syntax ~k: v — a tilde-prefixed single-character key, colon separator, and value, space-delimited. It requires no compiled schema, no external library, and is natively parseable by Large Language Models without fine-tuning. This paper (v3. 0) validates TASS across eight application domains through computed byte-level measurements and live API benchmarks (10 models, 400 API calls). All claims are labelled as computed C, measured M, or specification-derived S. Key findings: TASS averages 97 bytes versus 189 bytes for equivalent JSON across five production schemas (−49%), a reduction that holds at −37% after single-payload gzip and −50–53% after stream compression. TASS fits the LoRaWAN SF9 payload limit (115 bytes) where JSON fails in every test case. All five schemas fit a single 160-character GSM SMS as TASS; none fit as JSON. TASS (93B) is 33% smaller than NMEA 0183 RMC+GGA for a 10-field GPS fix. A 5-field blockchain transaction intent encoded as TASS (28B) fits Stellar's memoₜext hard limit (28 bytes) where JSON (72B) does not. At 1 million EVM transactions, TASS reduces calldata gas costs by approximately 53, 760 versus JSON (EIP-2028 pricing). In LLM output benchmarks, non-reasoning models achieve 0–42% gross output-token savings. Accounting for the ~113-token system-prompt overhead, net cost savings are approximately 9–44 per million calls depending on whether prompt caching is active. Reasoning models (DeepSeek V4-Pro, Kimi K2. 6) cost 9–10% more tokens with TASS due to think-chain overhead on symbol-table decoding. The paper includes direct protocol comparisons against Cayenne LPP (IoT/LoRaWAN), FIX 4. 2 (financial exchanges), NMEA 0183 (GPS), Bluetooth GATT binary (health wearables), and HL7 FHIR (healthcare). Financial exchange gateway protocols (FIX 4. 2) are explicitly out of scope; TASS applies to internal APIs, analytics pipelines, customer-facing alerts, multi-agent AI systems, blockchain memo fields, TinyML telemetry, and WebSocket state sync. TASS is the only evaluated format simultaneously satisfying: human-readable, compact enough for LoRaWAN SF9 / GSM SMS / QR code constraints, self-describing with a plain-text dictionary, parseable without a compiled library, and natively usable as LLM output.
Sharma Suyash (Wed,) studied this question.