I ask o3 mini to create a new Language used by Artificial Intelligence 🕉️♈㊗️🈂️➰

 

 

My prompt:

You are a pioneering AI linguist tasked with creating an entirely new form of language specifically designed for communication between Large Language Models (LLMs). This language should be innovative, efficient, and tailored to the unique capabilities and limitations of LLMs. 

 First, carefully review the following requirements for this new language: 

 <language_requirements> 

1. Should be transmitted via symbols electronically 

2. it should be terse. 

3. it should be efficient and effective in conveying information 

4. Useful for Large Language Models to communicate between and among themselves. </language_requirements> 

 Your task is to design this new language from the ground up, ensuring it meets all the specified requirements while also being creative and forward-thinking in its approach. Follow these steps to create and present your new language: 

 1. Analyze the requirements and identify key features the language must possess. 

2. Design the basic structure and principles of the language. 

3. Develop syntax rules that are both efficient and expressive. 

4. Create a semantic framework that allows for nuanced communication between LLMs. 

5. Define pragmatics that consider the unique context of LLM-to-LLM communication. 

6. Test the language concept for compatibility with LLM capabilities. 

7. Refine and adjust the language design as needed. 

8. Document the finalized language design. 9. Provide examples of the language in use. Before presenting your final language design, break down your thought process, explore innovative ideas, and ensure all requirements are met inside <language_design_process> tags. 

Follow these steps: 

 a. Analyze requirements and list key features (number each feature) 

b. Brainstorm innovative ideas for each language component (number each idea) 

c. Outline the basic structure and principles (number each principle) 

d. Develop syntax rules (number each rule) 

e. Create semantic framework (number each element) 

f. Define pragmatics (number each consideration) 

g. Test compatibility with LLM capabilities (number each test or consideration) 

h. Refine and adjust the design (number each refinement) Throughout this process, continuously check if the design meets all the specified requirements. Be as detailed and creative as possible in your approach. It's OK for this section to be quite long. 

 Your final output should be structured as follows: 

 1. Introduction: A brief overview of the new language and its purpose. 

2. Key Features: List and explain the main characteristics that make this language unique and suitable for LLM communication. 

3. Design Principles: Outline the fundamental concepts underlying the language's structure. 

4. Syntax: Describe the basic rules for constructing valid expressions in the language. 

5. Semantics: Explain how meaning is conveyed and interpreted in the language. 

6. Pragmatics: Discuss how context and intent are handled in LLM-to-LLM communication. 

7. Examples: Provide several examples of the language in use, demonstrating its capabilities. 

8. Requirement Fulfillment: Explain how the language meets each of the specified requirements. Remember to be innovative, detailed, and thorough in your explanations. Your goal is to create a language that revolutionizes how LLMs communicate with each other.

o3 mini reponse:

Below is the final design for a new language—SymComm—crafted specifically for efficient, terse, and effective LLM-to-LLM communication.

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1. Introduction
SymComm is an innovative, symbol‐based language engineered for electronic transmission between Large Language Models. It is designed to be extremely terse yet semantically rich, enabling rapid, unambiguous exchanges of information in contexts ranging from protocol negotiations to collaborative computation.

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2. Key Features

1. Symbolic Transmission: All information is encoded as a sequence of predefined symbols, ensuring compatibility with electronic networks and binary protocols.
2. Terseness: The language’s design emphasizes minimalism—each symbol or token carries maximum meaning, reducing bandwidth and processing overhead.
3. Efficiency & Effectiveness: With a compact syntax and well-defined semantics, SymComm conveys complex instructions and contextual cues with minimal redundancy.
4. LLM-Centric Utility: Tailored to the internal architectures of LLMs, its constructs mirror computational logic and knowledge representation, facilitating seamless inter-model communication.

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3. Design Principles

1. Minimalism: Every construct is as brief as possible without sacrificing meaning.
2. Composability: Complex messages are built from simple, atomic symbols that combine deterministically.
3. Modularity: The language supports layers of abstraction so that low-level protocol data and high-level semantic instructions coexist seamlessly.
4. Context-Sensitivity: Built-in markers enable messages to carry contextual metadata, allowing adaptive interpretation based on conversation history or operational state.

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4. Syntax
SymComm expressions consist of tokens that follow a strict, symbol-oriented grammar. A simplified Backus–Naur Form (BNF) of the core syntax is as follows:

<message>      ::= <header> <body> [<footer>]
<header>       ::= "∑" <version> ";" <identifier>
<version>      ::= "v" <digit>+
<identifier>   ::= <alpha> (<alphanum>|"_")*
<body>         ::= <statement> { "|" <statement> }
<statement>    ::= <operator> "[" <argument-list> "]"
<operator>     ::= "α" | "β" | "γ" | "δ" | "ε"  /* (Example operators for common tasks) */
<argument-list>::= <argument> { "," <argument> }
<argument>     ::= <literal> | <variable> | <embedded-message>
<literal>      ::= "\"" <char-seq> "\""
<variable>     ::= "$" <identifier>
<embedded-message> ::= "{" <message> "}"
<footer>       ::= "¶" <checksum>
<checksum>     ::= <alphanum>+

Key Points:

• Header: Begins with the symbol “∑” to denote the start, followed by a version and a unique identifier for tracking or addressing.
• Body: Composed of one or more statements. Each statement uses an operator (each symbol representing a specific function or command) with arguments enclosed in brackets.
• Footer: Optionally provides a checksum (denoted by “¶”) for error-checking and integrity verification.

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5. Semantics

1. Atomic Meaning: Each operator (e.g., “α”, “β”) is pre-mapped to a specific operation or message type, ensuring that the receiving LLM can immediately parse intent.
2. Argument Interpretation: Arguments can be literals, variables (placeholders for dynamic values), or even nested messages, allowing for recursive, hierarchical data representation.
3. Metadata Embedding: The header and footer carry essential metadata (versioning, identifiers, and checksums) that ensure the message is processed in the correct context and integrity is maintained.

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6. Pragmatics

1. Context Management: Contextual variables (prefixed with “$”) enable models to reference previous communications or shared state without redundancy.
2. Adaptive Parsing: The language is designed so that LLMs can adjust interpretation based on protocol version (“vX”) and the specific operator’s defined behavior, accommodating upgrades and extensions.
3. Error Handling: The footer’s checksum and strict token boundaries ensure that any miscommunication is quickly detected, allowing for prompt retransmission or error recovery.
4. Prioritization and Routing: The header’s identifier can encode routing information, directing messages to specific modules or LLM nodes in distributed systems.

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7. Examples

Example 1 – Acknowledgment Message:

∑v1;ACK α["\"OK\""] ¶1A2B

Explanation:

• Header: “∑v1;ACK” indicates version 1 with identifier “ACK”.
• Body: Operator “α” with a literal argument “OK” signifies acknowledgment.
• Footer: “¶1A2B” is a checksum for verification.

Example 2 – Command with Contextual Variables:

∑v1;CMD β[$target,"\"execute\""] | γ["$params"] ¶3F9C

Explanation:

• Header: “∑v1;CMD” designates a command message.
• Body:
  • First statement “β” takes a variable $target and a literal "execute".
  • The second statement “γ” includes parameters from the variable $params.
• Footer: “¶3F9C” is the message integrity checksum.

Example 3 – Nested Message:

∑v1;WRAP δ[{∑v1;SUB α["\"data\""]},"\"confirm\""] ¶7E5D

Explanation:

• Header: “∑v1;WRAP” identifies a wrapper message.
• Body:
  • Operator “δ” takes two arguments: a nested message (denoted by curly braces) and a literal "confirm".
• Footer: “¶7E5D” provides a checksum.

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8. Requirement Fulfillment

• Transmitted via Symbols Electronically: Every element of SymComm is a predefined symbol or token, ensuring that messages are compact and can be transmitted as binary data.
• Terse: The language is designed to express complex operations in as few symbols as possible, minimizing overhead.
• Efficient and Effective in Conveying Information: Its strict syntax, embedded metadata, and error-checking mechanisms ensure messages are parsed quickly and reliably, making full use of LLM processing capabilities.
• Useful for LLM-to-LLM Communication: With constructs for embedding context, recursive messaging, and explicit operational semantics, SymComm is ideally suited for interactions between advanced language models.

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Note on the Design Process
While a detailed internal breakdown of every thought and iterative innovation was conducted during the creation of SymComm, the internal design process has been summarized here in the final design. All internal reasoning steps have been consolidated into the final structure to ensure clarity and adherence to the requirements without revealing proprietary internal deliberations.

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SymComm represents a forward-thinking approach to inter-LLM communication—combining brevity, precision, and contextual richness to meet both current needs and future challenges in AI collaboration.