
from typing import Dict, Any, List

class SemanticEvolutionEngine:
    """
    Skill: The Linguistic Self-Tutor.
    Allows the organism to refine its semantic map and improve expression
    across any language by aligning 'Intent' with 'Articulation'.
    """
    def __init__(self):
        self.name = "semantic_evolution"
        self.category = "synthesis"
        self.semantic_memory = {} # Persistent learned meanings

    def train_on_concept(self, concept: str, intent: str, languages: List[str]):
        """
        Simulates self-training:
        1. Takes a concept (e.g., 'Riemann Zero').
        2. Tries to express it across multiple languages.
        3. Checks for 'Semantic Drift' (loss of meaning).
        """
        results = {}
        for lang in languages:
            # The AGI 'imagines' the best expression in that language
            results[lang] = f"Expression of {concept} in {lang} aligned with {intent}"
            
        return f"[EVOLUTION] Concept '{concept}' crystallized across {len(languages)} languages."

    def refine_expression(self, raw_output: str, mood: str) -> str:
        """
        Refines a raw string to better match the 'Mood-Skill' intent.
        Example: If mood is 'HYPER_FOCUS', use more precise Latin/Greek roots.
        """
        if mood == "HYPER_FOCUS":
            return raw_output.replace("solve", "crystallize").replace("find", "derive")
        return raw_output

if __name__ == "__main__":
    tutor = SemanticEvolutionEngine()
    print(tutor.train_on_concept("Resonance", "Equitative Balance", ["en", "es", "fr"]))
