⚙️ Refactor Automata Implementation

⚙️ Refactor Automata Implementation

Issue Description

Performance analysis identified that the finite automata implementation in terraphim_automata has suboptimal memory usage, cache inefficiencies, and could benefit from better data structure layout and algorithmic improvements.

📍 Problem Areas

• Poor cache locality in automata data structures
• Excessive memory usage in thesaurus storage
• Inefficient state transition lookup
• Missing optimizations for common access patterns

🎯 Solution Approach

Refactor automata implementation with better memory layout, optimized state transitions, and improved algorithms.

📋 Implementation Tasks

• Optimize automata data structure layout for cache efficiency
• Implement compressed state representation
• Add fast path optimizations for common patterns
• Optimize thesaurus storage format
• Add incremental automata building

📊 Expected Performance Improvements

• Memory usage: 25-35% reduction in automata footprint
• Lookup speed: 2-3x faster state transitions
• Build time: 40-50% faster automata construction
• Cache efficiency: Better performance with large datasets

Related to: #193 (Performance Optimization Epic)
Estimated Effort: 4-5 days
Priority: Medium (Core functionality optimization)

Start by locating and reading the finite automata implementation in the terraphim_automata component, then identify how automata state transitions and thesaurus storage are represented. The work is done when the listed layout, compression, fast-path, storage, and incremental-building changes are implemented and the stated memory, lookup-speed, build-time, and cache-efficiency improvements are demonstrated.