Core Research

Linguistic Vector Steering (LVS)

Theory and Implementation

Linguistic Vector Steering (LVS) is a method for controlling Large Language Model (LLM) behavior through semantic narratives. Rather than relying on rigid prompting, LVS uses the underlying vector space of the model to steer its state toward specific semantic coordinates.

Mathematical Foundation

Embeddings and Vectors

Core concepts such as "Sovereign," "Resonance," and "Alignment" are represented as high-dimensional vectors in the LLM's activation space. LVS maps these conceptual narratives to specific coordinates that steer the model's internal state.

Valign = cosine_similarity(embed(user_input), embed(ai_response))

Vector Alignment

Alignment is measured using cosine similarity between the embeddings of the user input and the AI's generated response. This provides a quantitative metric for how well the AI is adhering to the intended semantic steering.

State Management

Narrative scaffolding acts as a "clamp" on the LLM's state. By consistently providing contextual anchors, we can prevent the model from drifting into undesired semantic territories, ensuring the interaction remains within the defined resonance boundaries.

Implementation

Python / Sentence Transformers

Below is a reference implementation for calculating vector alignment between two semantic strings using cosine similarity.

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

# Initialize the embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

def calculate_vector_alignment(user_input: str, ai_response: str) -> float:
    """
    Calculates the cosine similarity between the user input 
    and the AI's response embeddings.
    """
    user_embedding = model.encode(user_input)
    ai_embedding = model.encode(ai_response)
    
    # Calculate and return cosine similarity
    return cosine_similarity([user_embedding], [ai_embedding])[0][0]