Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

==== Think of it this way: ====
* JSON: “Here is the exact structure and all the data. Please parse it.”
* YAML / TOON: “Same idea, but with a bit less syntax fat.”
* LoreTokens: “You already know the structure, the domain, the style, and the rules. Here’s a tiny semantic pointer—go do the right thing.”

Same universe, completely different game.

===== JSON / YAML / TOON =====
* Optimize syntax.
* They reduce characters around field names and nesting, but they still ship: - Every key ("users", "name", "role", etc.) - Every structural marker ({}, [], :, ,, dashes, line breaks)
* Great as a serialization format, but they’re describing layout more than meaning.

LoreTokens
* Optimize meaning.
* A single LoreToken can encode: - Domain: neurology, endocrinology, trading, whatever. - Task: explain, summarize, classify, generate training data, etc. - Style: formal, playful, bulleted, academic, patient-friendly, etc. - Constraints: safety, ethics, level of detail, formatting.
* Instead of shipping full schemas and instructions, you ship a tiny semantic handle that tells the model: >

Result: JSON/TOON compress how we draw the boxes. LoreTokens compress what those boxes ''mean''.

===== JSON / YAML / TOON were built so humans and traditional programs can: =====
* Read them easily
* Validate them with rigid schemas
* Serialize/deserialize consistently

LLMs don’t care about curly braces or commas—they care about patterns of concepts.

LoreTokens are:
* AI-native control language They plug directly into the model’s internal semantic space. No extra “OK, now turn this schema into instructions” step.
* One hop away from behavior Instead of: >  you get: >

That cuts both latency and compute overhead: fewer tokens to read, fewer mapping steps.

===== Take your screenshot example: =====
* JSON: lots of quotes, braces, repeated keys
* TOON: slimmer, but still carrying explicit field names and structure

Now imagine:
* Repeating that structure in millions of prompts
* Or packing huge configs / logs / memories into an LLM’s context window

With LoreTokens:
* The schema (“users table with id / name / role”) can live as an internal concept or a separate definition.
* A single short LoreToken can mean: >

Instead of pasting a giant block of JSON/TOON every time, you send something closer to:

<syntaxhighlight lang="text">LT:users.v3.filter:admins.summarize:short

</syntaxhighlight>

(Stylistic example, not real syntax.)

That’s dozens to hundreds of tokens saved per request, multiplied by:
* Every agent
* Every call
* Every second

This is where power savings and throughput gains become very real.

===== With JSON / TOON training data: =====
* Every example carries all its structural baggage: - Repeated field names - Repeated braces/brackets/punctuation
* The model has to: 1. Learn the format (JSON vs TOON vs YAML) 2. Learn the mapping from that format → meaning 3. Only then learn the actual task (classification, reasoning, generation…)

This wastes a lot of your training budget on “remember how this format works.”

With LoreTokens as part of training:
* You teach the model once: - "LT_MED_NEURO_DOC_V1" → “long, structured, clinically sound explanation of neurology for non-experts” - "LT_TRADE_SIGNAL_V3" → “compressed multi-indicator trading decision with confidence + reasoning”
* After that, every single training sample that uses that LoreToken: - Reinforces the same concept - Adds nuance (edge cases, special phrasing, exceptions) - Doesn’t have to re-explain the structure or instructions

Benefits:
# Higher semantic density per token More gradient signal dedicated to “what this concept is” instead of “remember the punctuation.”
# Better generalization Many samples sharing the same LoreToken cause its embedding to become a dense, high-quality “concept node” in the model.
# Smaller datasets for the same capability Because instructions and structure are factored out into reusable semantic handles, you can cover more ground with fewer raw tokens.

TL;DR: JSON/TOON help encode data. LoreTokens help encode behavior and understanding.

===== In complex AI systems (like Nova, or multi-agent orchestration): =====
* You need: - Configs - Role definitions - Safety rails - Specialized skills - Cross-agent messages

JSON / TOON approach:
* Every message: chunk of structured text
* Each agent: has to parse, interpret, and reprompt from it
* Lots of repeated boilerplate in context every time

LoreToken approach:
* Agents pass around semantic tickets, e.g.: - “LT_CASE:diabetes.v4.summary_for_patient” - “LT_LOG:trade.2025-02-06.review_shortfall” - “LT_SECURITY:high_risk_injection”

The receiving model:
* Already knows what each of those handles means (from prior training / configuration)
* Can jump straight into the right format and reasoning pattern

That gives you:
* Lower bandwidth between agents
* Smaller contexts
* Faster, more reliable behavior handoff

===== TOON’s claim: =====
* As data structures grow, TOON stays slimmer than JSON/YAML by reducing punctuation and keywords.

True for structure-only compression.

LoreTokens sidestep the problem:
* You don’t keep nesting more text to show structure.
* You define one LoreToken (or a tiny family of them) that represent: - A template - A procedure - A schema - A whole workflow
* Deeply nested logic is handled inside the model itself (or the LoreToken spec), not exploded into literal text every time.

So instead of:

<syntaxhighlight lang="toon">users[1000]{id,name,role}:
  ...

</syntaxhighlight>

You might have:

<syntaxhighlight lang="text">LT_DATASET:users.v5.1000rows

</syntaxhighlight>

The semantics of the 1000 rows live in the model / backend; the LoreToken is the high-compression reference.

===== LoreTokens don’t kill JSON/YAML/TOON; they wrap and outrank them in AI-heavy layers. =====
* Use JSON / TOON when: - Interfacing with traditional APIs - Storing data in files or DBs - You need strict, human-auditable schema
* Use LoreTokens when: - Talking to and between AI systems - Orchestrating behavior, style, and intent - Compressing memories, logs, schemas, and prompts into semantic “atoms”

In other words:

: 

If you want, next step we can:
* Take a single medical or trading LoreToken,
* Sketch its “JSON view,” “TOON view,” and “LoreToken view,”
* And show exactly how many characters / tokens each approach burns for the same effective capability.