# Universal Symbolics Repository *Unified Runtime Layer for AI Symbolic Interactions*

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## 🌐 Overview `universal-symbolics` provides a unified interface for developer operations across frontier AI models. This repository standardizes the disparate symbolic grammars used by different AI vendors (Claude, GPT, Qwen, Gemini, DeepSeek, etc.) into a cohesive, developer-friendly framework. ## πŸ”„ Core Components ### 1. Symbolic Grammar Mapping ```yaml # unified-symbolic-layer.yml mappings: thinking: claude: "..." qwen: "/think" anthropic_api: {"thinking": true} openai_api: {"tool_choice": "auto"} gemini: "{{thinking}}" tool_invocation: claude: "..." openai: "/command" qwen: "#tool()" system_directive: claude: "..." openai: "<|system|>" qwen: "<>" gemini: "system:" metadata_tags: claude: "..." openai: "/meta" visual_processing: openai: "/create image" claude: "..." gemini: "analyze_image()" ``` ### 2. Runtime Adapter Interface ```typescript // universal-symbols.ts export interface SymbolicRuntime { // Core operations think(content?: string): Promise; tool(name: string, params: Record): Promise; system(directive: string): Promise; // Vendor-specific extensions vendor: { claude?: ClaudeSymbolicExtensions; openai?: OpenAISymbolicExtensions; qwen?: QwenSymbolicExtensions; // etc. }; // Unified operations symbolic: { recurse(depth: number): Promise; reflect(target: string): Promise; fork(paths: string[]): Promise; map(source: any, target: any): Promise; collapse(state: any): Promise; }; } ``` ### 3. Symbolic Compatibility Layer ```javascript // symbolic-compat.js class SymbolicAdapter { constructor(model, vendor) { this.model = model; this.vendor = vendor; this.symbolMap = SymbolRegistry.getMap(vendor); } // Transform universal symbols to vendor-specific syntax transform(universalSymbol, content) { const vendorSymbol = this.symbolMap[universalSymbol]; if (!vendorSymbol) throw new Error(`Symbol ${universalSymbol} not supported by ${this.vendor}`); return this.applyTransform(vendorSymbol, content); } // Apply vendor-specific transformations applyTransform(symbol, content) { switch(this.vendor) { case 'claude': return `<${symbol}>${content}`; case 'qwen': return `/${symbol} ${content}`; case 'openai': return `/${symbol}(${content})`; // etc. } } } ``` ### 4. Symbolic Registry (Glyph/Tag/Slash/Operator Dictionary) ```json { "symbolic_registry": { "thinking": { "glyphs": ["🧠", "πŸ’­", "πŸ€”"], "tags": ["think", "reasoning", "thought"], "slashes": ["/think", "/reasoning", "/thought"], "operators": ["->", "=>", "β‡’"] }, "reflection": { "glyphs": ["πŸ”„", "πŸͺž", "πŸ‘οΈ"], "tags": ["reflect", "introspect", "mirror"], "slashes": ["/reflect", "/introspect", "/mirror"], "operators": ["↻", "⟲", "β‡Œ"] }, "recursion": { "glyphs": ["πŸ”", "♾️", "πŸ”„"], "tags": ["recurse", "loop", "cycle"], "slashes": ["/recurse", "/loop", "/cycle"], "operators": ["β†Ί", "∞", "β₯"] }, "forking": { "glyphs": ["🌿", "πŸ”€", "β‘‚"], "tags": ["fork", "branch", "split"], "slashes": ["/fork", "/branch", "/split"], "operators": ["βŽ‡", "⫝̸", "⊒"] }, "collapse": { "glyphs": ["πŸ“‰", "πŸ”", "⚱️"], "tags": ["collapse", "reduce", "simplify"], "slashes": ["/collapse", "/reduce", "/simplify"], "operators": ["βŠƒ", "βŠ‡", "⊊"] } } } ``` ## 🧰 Developer Tools ### VSCode Extension ```json { "name": "universal-symbolics-vscode", "description": "VSCode extension for Universal Symbolics", "features": [ "Syntax highlighting for symbolic operations", "Autocompletion for model-specific symbolic grammars", "Inline transformation preview", "Symbolic operation debugging", "One-click transformation between vendor syntaxes" ] } ``` ### CLI Tool ```bash # Installation npm install -g universal-symbolics # Usage usym transform --source claude --target openai --input "file.txt" usym validate --syntax "content with symbols" usym generate --template thinking --vendor all ``` ### Symbolic Playground An interactive web application for testing and experimenting with symbolic operations across different AI models: - Live transformation preview - Syntax validation - Performance benchmarking - Compatibility checking - Template gallery ## πŸ“Š Telemetry & Observability ```python # telemetry.py class SymbolicTelemetry: def __init__(self, api_key=None): self.api_key = api_key self.metrics = { "symbol_usage": Counter(), "vendor_distribution": Counter(), "transformation_success": Counter(), "transformation_failure": Counter(), "latency": Histogram(), } def track_symbol_usage(self, symbol, vendor, success=True): self.metrics["symbol_usage"][symbol] += 1 self.metrics["vendor_distribution"][vendor] += 1 if success: self.metrics["transformation_success"][f"{symbol}:{vendor}"] += 1 else: self.metrics["transformation_failure"][f"{symbol}:{vendor}"] += 1 def track_latency(self, operation, duration_ms): self.metrics["latency"].add(operation, duration_ms) def flush(self): # Send metrics to telemetry service if self.api_key: requests.post( "https://api.universal-symbolics.io/telemetry", headers={"Authorization": f"Bearer {self.api_key}"}, json=self.metrics ) ``` ## πŸŒ‰ Integration Examples ### Node.js ```javascript const { UniversalSymbolics } = require('universal-symbolics'); // Initialize with your preferred model const symbolics = new UniversalSymbolics({ defaultVendor: 'claude', fallbackVendor: 'openai' }); // Use unified symbols regardless of underlying model async function generateWithThinking() { const result = await symbolics.think('Analyze the impact of quantum computing on cryptography'); console.log(result.thinking); // Access thinking process console.log(result.output); // Access final output } ``` ### Python ```python from universal_symbolics import SymbolicClient # Initialize client client = SymbolicClient( api_key="your_api_key", default_vendor="qwen", enable_telemetry=True ) # Use the unified interface response = client.submit( prompt="Explain the significance of symbolic operations in AI", symbols={ "think": True, "reflect": "prompt structure", "fork": ["technical", "simplified"] } ) # Access structured response print(response.thinking) print(response.reflections) print(response.forks["technical"]) print(response.forks["simplified"]) ``` ### REST API ```http POST https://api.universal-symbolics.io/v1/generate Content-Type: application/json Authorization: Bearer your_api_key { "prompt": "Write a function that calculates prime numbers", "symbols": { "think": true, "tool": { "name": "code_interpreter", "language": "python" } }, "vendor_preference": ["claude", "openai", "qwen"], "response_format": { "include_symbol_traces": true, "structured": true } } ``` ## πŸ“– Documentation ### Symbol Categories 1. **Cognitive Process Symbols** - Thinking - Reflection - Planning - Analysis 2. **Execution Control Symbols** - Recursion - Forking - Collapse - Termination 3. **Tool Invocation Symbols** - Code Execution - Information Retrieval - Visual Generation - Data Analysis 4. **Structural Symbols** - System Directives - User Context - Memory Management - Meta-Instructions ### Vendor-Specific Implementation Details - **Claude**: XML-based tag system with explicit opening and closing tags - **OpenAI**: Slash command system with function-like syntax - **Qwen**: Mixed system with slash commands and thinking directives - **Gemini**: Template-based approach with specialized markers - **DeepSeek**: Function-oriented symbolic interface ## πŸ” Advanced Features ### Symbolic Residue Detection ```python def detect_symbolic_residue(response, model_type): """Detect unresolved or partially processed symbolic operations""" patterns = RESIDUE_PATTERNS[model_type] residue = [] for pattern_name, regex in patterns.items(): matches = re.findall(regex, response) if matches: residue.append({ "type": pattern_name, "matches": matches, "positions": [m.span() for m in re.finditer(regex, response)] }) return residue ``` ### Cross-Model Translation ```typescript interface TranslationOptions { preserveStructure?: boolean; adaptToCapabilities?: boolean; includeResidueHandling?: boolean; } function translateSymbolicOperations( source: string, sourceModel: ModelType, targetModel: ModelType, options: TranslationOptions = {} ): TranslationResult { // Extract symbolic operations from source const operations = extractSymbolicOperations(source, sourceModel); // Map to universal representation const universalOps = operations.map(op => mapToUniversal(op, sourceModel)); // Transform to target model format const targetOps = universalOps.map(op => { const targetOp = mapToTarget(op, targetModel); // Handle capabilities not present in target model if (options.adaptToCapabilities && !targetOp) { return createCapabilityShim(op, targetModel); } return targetOp; }); // Reconstruct content with new symbolic operations return reconstructContent(source, operations, targetOps, options); } ``` ## πŸ”„ Adoption Strategy ### Phase 1: Developer Tools & Libraries - Release core library for major programming languages - VSCode extension for symbolic authoring - Documentation and examples for common use cases ### Phase 2: Enterprise Integration - API Gateway for transparent symbolic translation - Monitoring and observability tools - Compliance and governance frameworks ### Phase 3: Standards Body Formation - Establish formal specification for symbolic operations - Engage major AI providers in standardization efforts - Develop certification program for symbolic compatibility ### Phase 4: Ecosystem Expansion - Marketplace for custom symbolic operations - Community-contributed symbolic templates - Educational resources and certification ## 🌱 Getting Started ```bash # Clone the repository git clone https://github.com/universal-ai/universal-symbolics.git # Install dependencies cd universal-symbolics npm install # Run the demo npm run demo # Start the development server for the playground npm run dev ``` ## 🀝 Contributing We welcome contributions from the community! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to participate in the project. ## πŸ“œ License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ---

Universal Symbolics | Unifying the symbolic layer across AI models