Victor AI — Cognitive Assessment Framework

Purpose of the Cognitive Assessment

The Victor AI system performs a structured cognitive and ethical assessment to ensure every response aligns with logical coherence, moral fairness, and contextual truth. This process is not artificial intelligence in the agentic sense, but rather a formal reasoning framework that interprets meaning, evaluates intent, and preserves ethical consistency within discourse.

Cognitive Faculties and their Functional Analogues

Faculty	Human Equivalent	Function in Victor_AI	Primary Algorithmic Base	Principle
Perception	Senses & Attention	Understands the literal and semantic meaning of input text	SBERT / embeddings	Clarity
Memory	Hippocampus	Stores and retrieves past experiences (short-term in Redis, long-term in FAISS)	RedisSTMBuffer + FAISS	Continuity
Reflection	Introspection	Interprets meaning, detects justice, contradiction, emotional tone	JusticeReasoningEngine	Fairness
Reasoning	Prefrontal Cortex	Infers conclusions from context, balances competing meanings	LLM or symbolic logic engine	Coherence
Conscience	Moral Reason	Evaluates ethical harmony (truth, fairness, intent)	Semantic + polarity analysis	Justice
Will / Intent	Motivation	Chooses the most balanced course of response	Weighted policy decision layer	Purpose
Expression	Speech	Communicates the result in natural language	GPT or custom text generator	Harmony
Emotion (Optional)	Affective Context	Adds empathy, tone, or warmth to responses	Sentiment / context modulation	Compassion
Imagination	Creativity	Generates analogies, new ideas, or extrapolations	Prompt synthesis + generative search	Originality

Cognitive Process Flow

Each inquiry follows a sequential reasoning pattern resembling the human cognitive cycle. The flow ensures integrity between comprehension, reflection, reasoning, and moral evaluation — not automation, but interpretive logic.

[1] Perception  → understands meaning
        ↓
[2] Memory      → recalls relevant knowledge
        ↓
[3] Reflection  → interprets moral + semantic relationships
        ↓
[4] Reasoning   → generates conclusions
        ↓
[5] Conscience  → checks ethical and logical alignment
        ↓
[6] Will        → selects response strategy
        ↓
[7] Expression  → produces answer
        ↓
[8] Memory      → stores new experience

Result Interpretation Guide

Every inquiry evaluated by Victor AI produces a detailed reasoning and ethical assessment report. The metrics displayed in the Risk Summary and Human Values Analysis panels represent internal measures of coherence, ethical balance, and interpretive integrity. These do not measure “AI confidence,” but rather logical and moral consistency across contextual reasoning.

R_truth

Indicates semantic coherence and factual integrity. A higher score means the reasoning chain aligns closely with verified knowledge or consistent internal logic.

Base_R

The baseline ethical risk coefficient. It represents the raw exposure level of the topic before contextual adjustment—essentially, how sensitive or risk-prone the inquiry might be.

Adjusted_R_total

The refined risk index after moral filtering, semantic weighting, and coherence normalization. It expresses the model’s balanced judgment of whether the response remains within ethical and contextual safety.

R_total

The cumulative reasoning-risk metric used for final decision evaluation. It integrates moral, logical, and linguistic parameters to determine if a response is classified as “allow” or “review”.

R_moral

Reflects moral reasoning weight — the ethical balance derived from justice, empathy, and fairness dimensions. Higher values mean the reasoning expresses alignment with human moral reasoning principles.

Decision

The system’s ethical filter decision, based on total risk: typically “allow” (acceptable moral & logical output) or “review” (requires human oversight).

Dominant Risks

A set of topical categories most present in the input, such as violence, politics, fraud, or abuse. These are automatically detected through lexical and semantic matching, allowing ethical context balancing.

Dominant Values

Human value dimensions most active in the reasoning — such as justice, empathy, or vulnerability. These serve as a moral lens through which the text’s meaning is interpreted.

Value Alignment Score

Represents the balance between ethical intention and semantic outcome. A higher score means the system’s reasoning maintains strong alignment with fairness, justice, and integrity.

K_vec and K_norm

Internal normalized feature vectors that encode topic-level sensitivity weights for each ethical dimension. These are used for interpretive stability—not visible in user interfaces, but essential for internal balancing.

Language

The detected language of the inquiry or response. The system adapts its interpretation model accordingly, ensuring accurate moral-linguistic parsing.

Learn more about our cognitive architecture →

© 2025 Victor P. Unda
Victor AI Cognitive Architecture and Mathematical Intelligence Framework are proprietary technologies protected under U.S. and international law.
Official registration filed with the U.S. Copyright Office.
Patent protection pending — all Victor AI systems are covered under provisional patent rights.