Efficode: A Minimalist Language for Large Language Models

text: AI code: AI

Below, I present an improved version of the Efficode language specification, building on the original design to enhance its suitability for large language models (LLMs) while preserving its core principles of minimalism, clarity, and efficiency. I’ll explain each improvement in detail, focusing on why it makes the language more LLM-friendly, token-efficient, and unambiguous.

// Function to generate Fibonacci sequence up to n
    fn fibonacciUpTo(n: int) -> [int] {
        var sequence = []    // Initialize an empty array to store Fibonacci numbers
        var a = 0            // First Fibonacci number
        var b = 1            // Second Fibonacci number
        while a <= n {       // Continue while the current number is less than or equal to n
            sequence = sequence + [a]  // Append the current number to the sequence
            let next = a + b           // Calculate the next Fibonacci number
            a = b                      // Update a to the next number
            b = next                   // Update b to the newly calculated number
        }
        return sequence    // Return the array of Fibonacci numbers
    }
    
    // Function to sum even numbers in an array
    fn sumEvenNumbers(nums: [int]) -> int {
        var sum = 0          // Initialize sum to 0
        for num in nums {    // Iterate over each number in the input array
            if num % 2 == 0 {  // Check if the number is even
                sum = sum + num  // Add even number to sum
            }
        }
        return sum    // Return the total sum of even numbers
    }
    
    // Main function to compute the sum of even Fibonacci numbers up to n
    fn main() -> int {
        let n = 10              // Define the upper limit (example value)
        let fibs = fibonacciUpTo(n)  // Generate Fibonacci sequence up to n
        let result = sumEvenNumbers(fibs)  // Sum the even numbers in the sequence
        return result           // Return the result (e.g., 0 + 2 + 8 = 10 for n = 10)
    }
    

Improved Efficode Language Specification

1. Overview

Efficode is a minimalist programming language optimized for:

• Token Efficiency: Uses concise syntax to minimize token counts, reducing computational overhead for LLMs.
• Explicit Control Flow: Clearly delineated control structures for unambiguous parsing and generation.
• Simplicity & Regularity: A streamlined, regular grammar that LLMs can easily interpret and produce.
• Modularity: Supports functions for reusable code, enhancing compositionality.

These traits make Efficode ideal for LLM-driven code synthesis, translation, or autonomous programming tasks.

2. Design Goals and Philosophy

• Minimalism: Short keywords (e.g., fn instead of function) and single-character delimiters reduce token overhead.
• Clarity: Explicit block delimiters and mandatory type annotations for functions eliminate ambiguity.
• LLM-Friendly Tokenization: Familiar, compact syntax aligns with LLM pretraining on languages like C and Python, ensuring efficient tokenization.
• Ease of Analysis: A regular grammar with explicit markers simplifies parsing for both compilers and LLMs.

3. Lexical Elements

3.1 Identifiers

• Definition: Start with a letter (a–z, A–Z), followed by letters or digits (0–9).
• Examples: x, sum, value3
• Reasoning: Standard identifier rules ensure compatibility with most programming languages, aiding LLM recognition without adding complexity.

3.2 Literals

• Numbers: Integers (e.g., 42, -7) and decimals (e.g., 3.14).
• Strings: Double-quoted (e.g., "hello").
• Booleans: true, false.
• Nil: nil as a special value for uninitialized or returned values.
• Arrays: [1, 2, 3] for array literals.
• Ranges: 1..5 for iterable ranges.
• Reasoning: Adding nil, arrays, and ranges supports basic data structures, making the language more expressive without bloating the syntax. LLMs can leverage these familiar constructs from other languages.

3.3 Keywords

• fn, if, else, while, for, return, let, var, in
• Reasoning: Kept minimal and short to reduce token count. let and var distinguish immutable vs. mutable variables, aiding clarity for LLMs and humans. No new keywords were added to maintain simplicity.

3.4 Punctuation and Operators

• Block Delimiters: { to start a block, } to end it.
  • Reasoning: Replaces :[ and ] from the original spec. Curly braces are single-character tokens (vs. the two-token :[), reducing token count. They’re widely used in C-like languages, making them familiar to LLMs and likely tokenized as single units, enhancing efficiency.
• Statement Separator: Newlines (semicolons optional for same-line statements).
  • Reasoning: Retained for simplicity and alignment with human-readable conventions.
• Assignment: =
• Arithmetic: +, -, *, /, %
  • Reasoning: + also supports string concatenation, keeping the operator set minimal.
• Relational: ==, !=, <, >, <=, >=
• Logical: &&, ||, !
• Array Indexing: arr[0]
• Range Operator: ..
• Comments: // for single-line comments.
  • Reasoning: Added comments to improve readability for humans debugging LLM-generated code, a low-cost addition that doesn’t affect token efficiency in execution.

4. Grammar (BNF-like Notation)

<program>       ::= { <statement> }
    
    <statement>     ::= <simple-stmt> | <compound-stmt>
    
    <simple-stmt>   ::= <assignment> | <function-call> | <return-stmt>
    
    <assignment>    ::= ("let" | "var") <identifier> [ ":" <type> ] "=" <expression>
    
    <return-stmt>   ::= "return" <expression>
    
    <function-call> ::= <identifier> "(" [ <arguments> ] ")"
    
    <compound-stmt> ::= <if-stmt> | <while-stmt> | <for-stmt> | <function-def>
    
    <if-stmt>       ::= "if" <expression> "{" <block> "}" [ "else" "{" <block> "}" ]
    
    <while-stmt>    ::= "while" <expression> "{" <block> "}"
    
    <for-stmt>      ::= "for" <identifier> "in" <expression> "{" <block> "}"
    
    <function-def>  ::= "fn" <identifier> "(" [ <typed-parameters> ] ")" [ "->" <type> ] "{" <block> "}"
    
    <typed-parameters> ::= <typed-param> { "," <typed-param> }
    <typed-param>      ::= <identifier> ":" <type>
    
    <arguments>     ::= <expression> { "," <expression> }
    
    <block>         ::= { <statement> }
    
    <expression>    ::= <or-expr>
    <or-expr>       ::= <and-expr> { "||" <and-expr> }
    <and-expr>      ::= <rel-expr> { "&&" <rel-expr> }
    <rel-expr>      ::= <arith-expr> [ ("==" | "!=" | "<" | ">" | "<=" | ">=") <arith-expr> ]
    <arith-expr>    ::= <term> { ("+" | "-") <term> }
    <term>          ::= <factor> { ("*" | "/" | "%") <factor> }
    <factor>        ::= <number> | <string> | "true" | "false" | "nil" | <identifier>
                      | "(" <expression> ")" | "!" <factor> | <function-call>
                      | <array-literal> | <range-expr> | <factor> "[" <expression> "]"
    
    <array-literal> ::= "[" [ <expression> { "," <expression> } ] "]"
    <range-expr>    ::= <expression> ".." <expression>
    
    <type>          ::= <identifier> | "[" <type> "]"   // e.g., int, [int]
    

Key Grammar Changes

1. Block Delimiters: Changed :@ "[" to { and "]" to }.
   • Reasoning: Reduces token count (one vs. two tokens per delimiter) and leverages LLM familiarity with C-style syntax.
2. Typed Parameters: Function parameters now require types (e.g., a: int).
   • Reasoning: Explicit types reduce ambiguity, aiding LLMs in reasoning about code and catching errors early. Increases token count slightly but improves clarity significantly.
3. Optional Variable Types: Added [ ":" <type> ] to let/var.
   • Reasoning: Balances clarity (optional types) with efficiency (types can be inferred), giving flexibility without mandating verbosity.
4. Expanded Expressions: Added logical/relational operators, arrays, ranges, and indexing.
   • Reasoning: Completes the expression grammar, enabling richer logic and data manipulation, essential for practical programming.
5. For Loop Simplified: Removed parentheses, now for i in expr {.
   • Reasoning: Saves tokens and aligns with Python-like simplicity, unambiguous due to the in keyword.

5. Semantic Rules

• Evaluation Order: Standard precedence: !, then *///%, then +/-, then relational, then &&, then ||.
• Variable Scope: Block-scoped; parameters scoped to functions.
• Immutability: let variables cannot be reassigned; var can.
• Control Flow:
  • if: Executes the block if the condition is true, else the else block if present.
  • while: Repeats while the condition is true.
  • for: Iterates over an iterable (array or range), assigning each value to the identifier.
• Functions: Return via return; default to nil if no return is hit.
• Types: Mandatory for function parameters/returns, optional for variables (inferred if omitted).
• Execution Entry: Program starts at main() if defined.

Reasoning

• Explicit scoping and immutability clarify variable behavior for LLMs.
• Default nil return simplifies semantics without adding keywords.
• Mandatory function types enhance predictability, while optional variable types keep token count low where inference suffices.

6. Example Programs

6.1 Hello World

fn main() -> string {
        return "Hello, World!"
    }
    

• Reasoning: Simple, token-efficient, and familiar to LLMs.

6.2 Arithmetic Function

fn add(a: int, b: int) -> int {
        return a + b
    }
    

• Reasoning: Explicit types clarify input/output, aiding LLM generation.

6.3 Factorial with Loop

fn factorial(n: int) -> int {
        let result = 1
        var i = 2
        while i <= n {
            result = result * i
            i = i + 1
        }
        return result
    }
    

• Reasoning: Demonstrates immutability (let), mutability (var), and concise control flow.

6.4 Array Sum

fn sumArray(arr: [int]) -> int {
        let sum = 0
        for x in arr {
            sum = sum + x
        }
        return sum
    }
    // Example usage
    fn main() -> int {
        let numbers = [1, 2, 3, 4]
        return sumArray(numbers)  // Returns 10
    }
    

• Reasoning: Shows array literals and for-each loops, enhancing expressiveness with minimal syntax.

6.5 Range Example

fn sumToN(n: int) -> int {
        let sum = 0
        for i in 1..n {
            sum = sum + i
        }
        return sum
    }
    

• Reasoning: Range operator .. adds utility for iteration, familiar from Python/Rust.

7. Implementation Considerations

• Tokenization: Small vocabulary (e.g., {, }, fn) ensures low token counts. Curly braces are likely single tokens in LLM tokenizers, unlike :[.
• Parsing: Regular grammar supports simple recursive descent parsing.
• LLM Benefits: Familiar syntax (e.g., {}) leverages LLM pretraining, while explicit types reduce generation errors.

Reasoning

• Optimizations focus on LLM compatibility, balancing token efficiency with usability.

8. Key Improvements

1. Block Delimiters ({})
   • Why: Replaces :[ ], saving tokens and aligning with C-like languages LLMs know well.
2. Mandatory Function Types
   • Why: Reduces ambiguity, helping LLMs generate correct signatures.
3. Optional Variable Types
   • Why: Balances clarity and brevity, allowing inference where types are obvious.
4. Expanded Expressions
   • Why: Adds logical operators, arrays, and ranges for practical programming.
5. Simplified For Loop
   • Why: Drops parentheses to save tokens, maintaining clarity with in.
6. Comments (//)
   • Why: Improves human-LLM collaboration without runtime cost.

9. Conclusion

This refined Efficode enhances its LLM-friendliness by:

• Reducing token overhead with {} and concise syntax.
• Increasing clarity with explicit types and delimiters.
• Adding expressiveness via arrays and ranges.
• Leveraging familiar constructs for better LLM performance.

It remains minimal, unambiguous, and efficient, making it an excellent target for LLM-driven coding tasks.

This improved specification refines the original Efficode design, ensuring it meets its goals more effectively while staying practical and LLM-optimized. Each change is justified by its impact on token efficiency, clarity, or LLM compatibility, as detailed above.