ZodicogAI — Hybrid Symbolic-Generative Compatibility Intelligence

ZodicogAI is a deployed hybrid symbolic-generative system for compatibility, communication, and relationship intelligence. It combines deterministic multi-framework profile computation across zodiac, MBTI, numerology, attachment, love styles, and related signals with schema-constrained LLM interpretation to generate grounded, explainable user-facing insights. Rather than relying on free-form generation, the system routes narrative output through pre-computed symbolic features and structured schemas.

---

Abstract

This work presents ZodicogAI, a deployed hybrid symbolic-generative system for compatibility, communication, and relationship intelligence. The system addresses a fundamental limitation of general-purpose LLMs when applied to interpersonal reasoning: the absence of grounded structural knowledge. By decomposing personality into ten independent deterministic dimensions — spanning zodiac archetypes, MBTI cognition, attachment patterns, love styles, numerology, and related signal families — and routing LLM inference through schema-constrained prompt templates, ZodicogAI produces outputs that are more specific, reproducible, and structurally coherent than conventional retrieval-augmented or in-context approaches.

Each numerical output is auditable and replicable. The LLM's role is interpretation of pre-computed structural data, not generative invention. This design choice eliminates the principal source of personality-context hallucination: competing ungrounded knowledge sources.

Key contributions:

1. A ten-dimension behavioral encoding scheme with deterministic compatibility matrices across all dimension pairs
2. A 13-class analysis type taxonomy with intent-driven prompt routing and schema-locked Pydantic output validation
3. A conversational oracle (Zodicognac) with session memory, full history passthrough, and per-session continuity across arbitrary turn depth
4. A dual-model inference architecture with proactive TTL-aware context cache management and reactive failure invalidation
5. A 360-profile celebrity database rendered fully statically, enabling zero-latency astrological profiling at scale
6. Reproducible evaluation on relationship reasoning tasks against generic LLM baselines

---

My Story

For years, I've been deeply invested in zodiac astrology, personality psychology, and relationship dynamics. I studied how a Scorpio ENFP navigates the world differently than a Gemini INFP — the layers of mythology, behavioral science, attachment styles, sexual archetypes, and numerological resonance that make people unique.

I searched everywhere for a tool that could synthesize this knowledge — something that honored both the ancient wisdom of the zodiac and the empirical rigor of modern psychology. Something that integrated MBTI, numerology, love languages, and attachment theory into a coherent system. But everything I found was fragmented. No tool truly understood the intersection.

So in 2026, I decided to build it myself. From scratch. The result is ZodicogAI — a system that treats multi-framework behavioral intelligence as structured computation: every insight is traceable to a deterministic engine, every score is reproducible, and the LLM's role is interpretation rather than invention.

The Problem:

General-purpose LLMs fail in domain-specific reasoning under structural constraints:

• Hallucination in personality contexts: Without grounding, LLMs generate plausible but internally contradictory personality descriptions. They lack fixed reference frames for zodiac archetypes or MBTI function stacks.
• Conflation of independent dimensions: Relationship dynamics involve ten or more structurally independent axes — emotional attachment, sexual polarity, numerological resonance, love language gaps, etc. Generic models conflate these rather than computing them independently and synthesizing afterward.
• Non-reproducibility: Sampling variance means identical queries produce different results. This is unacceptable for an analytical tool claiming scientific grounding.
• Insufficient specificity: "Does an INTJ Scorpio and ENFP Sagittarius work?" returns a generic personality overview rather than scored compatibility vectors across all ten dimensions.

The Solution: Decompose the problem into (1) deterministic symbolic computation grounded in established personality frameworks and (2) constrained LLM synthesis that interprets pre-computed structural outputs. The LLM does not generate facts — it interprets them. This reduces hallucination, enforces reproducibility, maintains full auditability, and concentrates LLM capability on the task it actually performs well: nuanced language synthesis over structured input.

---

System Architecture

User Profile(s) + Query
    │
    ▼
┌─────────────────────────────────────────────────────────┐
│              Analysis Type Router                        │
│   13-class taxonomy: hybrid_analysis, compatibility,    │
│   emotional, romantic, sextrology, love_style,          │
│   love_language, numerology, color, zodiac_article,     │
│   full_relationship_intelligence, celebrity, discover   │
└────────┬────────────────────────────────────────────────┘
         │
    ┌────┴──────┬──────────┬──────────┬──────────┬────────┐
    ▼           ▼          ▼          ▼          ▼        ▼
[Zodiac/    [MBTI +    [Numerology] [Sextrology] [Color] [Love
 Decan]    Behavioral]              Engine      Engine  Engines]
 Engine     Engine
    │           │          │          │          │        │
    └────┬──────┴──────────┴──────────┴──────────┴────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│          Multi-Dimensional Compatibility Matrices        │
│   ┌─────────────────────────────────────────────────┐  │
│   │ Behavioral Cosine Similarity (MBTI vectors)     │  │
│   │ Emotional Attachment Alignment (Bowlby × MBTI)  │  │
│   │ Romantic Polarity Balance (Jungian energetics)  │  │
│   │ Sexual Archetype Synastry (erotic typology)     │  │
│   │ Love Style Matrix (Lee 1973 — 6 styles)         │  │
│   │ Love Language Overlap (Chapman 1992 — 5 langs)  │  │
│   │ Numerological Resonance (Pythagorean 9×9)       │  │
│   │ Aura Color Harmony (HSL 3D distance)            │  │
│   │ Communication Pattern Match (F/T × E/I axes)   │  │
│   │ Attachment System Complementarity               │  │
│   └─────────────────────────────────────────────────┘  │
│   → Produces: Typed Pydantic schema, 50+ fields        │
└────────┬────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│         Context Assembly + Template Dispatch             │
│   13 purpose-built templates, each with:                │
│   { system_persona + structured_engine_output           │
│     + explicit format directives                        │
│     + intent-specific reasoning constraints }           │
└────────┬────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────┐
│   Gemini 2.5 Flash — Primary (JSON schema output)       │
│   └──> TTL-aware context cache (55-min proactive expiry)│
│   └──> Exponential backoff → Gemini 2.5 Flash Lite      │
│        (reactive fallback on 429 / 500 / timeout)       │
└────────┬────────────────────────────────────────────────┘
         │
         ▼
   Pydantic-validated structured JSON response

Core design principle: The LLM interprets symbolic computation outputs. It does not generate them. This provides:

• Auditability — every score is traceable to explicit, inspectable computation
• Reproducibility — identical inputs yield identical structural outputs; LLM variance exists only in language, not in data
• Specificity — attention is narrow and grounded rather than broadly generative
• Hallucination resistance — no competing knowledge sources; exactly one source of structural truth

---

Technical Methodology

1. Ten-Dimension Behavioral Encoding

Each user profile is encoded into a structured behavioral vector spanning ten independent dimensions:

Dimension A — Zodiac Archetype (Element × Modality × Decan)

• Basis: Western tropical zodiac with Hellenistic decan correspondence
• Computation: Birth date → sign classification → decan sub-ruler (day-of-sign ÷ 3)
• Resolution: 108 distinct decan profiles (12 signs × 3 decans × 3 sub-rulers)
• Output: ZodiacProfile(sign, element, modality, decan_label, decan_ruler, decan_keywords[])

Dimension B — Cognitive Type (MBTI Function Stack)

• Basis: Myers-Briggs 4-dichotomy model with Jungian cognitive function hierarchy
• Input: User-supplied type string or 8-question inline quiz (2 per dichotomy, score-based)
• Output: MBTIProfile(type, role_group, dominant, auxiliary, tertiary, inferior)

Dimension C — Behavioral Vector (Cosine Similarity)

• Computation:

  v_a = [E_score, S_score, T_score, J_score]     # ∈ [0,1]⁴
  v_b = [E_score, S_score, T_score, J_score]
  behavioral_similarity = cos_sim(v_a, v_b) × 100  # → [0, 100]
  

• Key property: Deterministic, parameter-free. Same input → identical output.

Dimension D — Emotional Attachment Orientation

• Basis: Attachment theory (Bowlby 1969, Ainsworth 1978) mapped to MBTI function axis
• Mapping: F-dominant → anxious-preoccupied spectrum; T-dominant → earned-secure spectrum; I → selective depth; E → broad stamina
• Output: EmotionalProfile(attachment_pattern, responsiveness_score, reassurance_need, conflict_avoidance_likelihood)

Dimension E — Romantic Polarity and Sexual Archetype

• Basis: Jungian anima/animus + modern sexual typology (Marks & Specht 1988)
• Polarity scoring: E > I > P > J → generative/pursuing; I > E > T > J → receptive/preserving
• Sextrology sub-module: 6-field typed schema: SextrologyAnalysis(sexual_character, erogenous_zones, fantasies, positions_and_dynamics, emotional_needs, long_term_fire)
• Output: RomanticProfile(polarity_type, passion_intensity, affection_pacing, sexual_archetype)

Dimension F — Love Style Distribution (Lee 1973)

• Styles: Eros (erotic), Storge (companionate), Ludus (playful), Mania (obsessive), Pragma (practical), Agape (selfless)
• Computation: MBTI type → fixed distribution weights → dominant + secondary styles
• Compatibility: 6×6 precomputed matrix, each cell ∈ [0, 100]
• Output: LoveStyleProfile(dominant, secondary[], compatibility_matrix_row)

Dimension G — Love Language Preference (Chapman 1992)

• Languages: Words of Affirmation, Acts of Service, Receiving Gifts, Quality Time, Physical Touch
• Computation: MBTI × zodiac element joint mapping (Fire → Physical Touch; Air → Words; Water → Quality Time; Earth → Acts of Service)
• Output: LoveLanguageProfile(primary, secondary, tertiary, language_gap_score_vs_partner)

Dimension H — Numerology (Pythagorean System)

• Computation:

  life_path    = digit_reduce(day + month + year)    # preserve 11, 22, 33
  expression   = digit_reduce(Σ letter_values(name))
  lucky_number = digit_reduce(life_path + expression)
  

• Compatibility:

  numerology_compat = lp_score × 0.50 + exp_score × 0.30 + cross_score × 0.20
  

• Signal: ≥70 → pursue; ≥55 → caution; <55 → avoid
• Output: NumerologyProfile(life_path, expression, lucky, compatibility_score, pursue_signal)

Dimension I — Aura Color Resonance (HSL Harmony)

• Computation:

  color_a    = ZODIAC_COLOR_MAP[sign_a]            # 12-entry dict, fixed hex values
  color_b    = ZODIAC_COLOR_MAP[sign_b]
  midpoint   = RGB_average(color_a, color_b)
  complement = HSL_rotate(midpoint, 180°)
  harmony    = 1 - (HSL_distance(color_a, color_b) / HSL_MAX)  # → [0, 100]
  

• Output: ColorProfile(aura_hex, harmony_score, middle_ground_hex, compatible_color_hex)

Dimension J — Communication Pattern

• Computation: MBTI F/T judgment axis × E/I energy axis → conflict resolution style scoring
• Output: CommunicationProfile(directness_score, emotional_intensity, conflict_approach, validation_need)

---

2. Multi-Dimensional Compatibility Synthesis

For pair analysis, ten independent scores are computed and synthesized into a weighted composite:

def compute_relationship_intelligence(a: Profile, b: Profile) -> RelationshipIntelligence:
    scores = {
        'behavioral':    cosine_similarity(a.mbti_vector, b.mbti_vector),
        'emotional':     attachment_alignment(a.attachment, b.attachment),
        'romantic':      polarity_resonance(a.romantic, b.romantic),
        'sexual':        sextrology_synastry(a.sexual, b.sexual),
        'love_style':    lee_compatibility_matrix(a.style, b.style),
        'love_language': language_channel_overlap(a.languages, b.languages),
        'numerology':    pythagorean_compatibility(a.numerology, b.numerology),
        'color':         hsl_harmony_distance(a.aura_color, b.aura_color),
        'communication': pattern_alignment(a.communication, b.communication),
        'attachment':    bowlby_complementarity(a.attachment, b.attachment),
    }

    overall = (
        scores['behavioral']    * 0.25 +
        scores['emotional']     * 0.20 +
        scores['romantic']      * 0.15 +
        scores['sexual']        * 0.12 +
        scores['love_style']    * 0.10 +
        scores['numerology']    * 0.08 +
        scores['communication'] * 0.05 +
        scores['color']         * 0.03 +
        scores['attachment']    * 0.02
    )

    viability = (
        "Excellent"    if overall >= 80 else
        "Good"         if overall >= 65 else
        "Challenging"  if overall >= 45 else
        "Incompatible"
    )

    return RelationshipIntelligence(scores=scores, overall=overall, viability=viability)

Key property: Every score is deterministic. Identical profiles always produce identical compatibility vectors, enabling stable ranking across thousands of profile pairs.

---

3. Analysis Type Taxonomy

ZodicogAI defines 13 discrete analysis types, each with a dedicated engine pipeline, Pydantic output schema, and prompt template:

Analysis Type	Input	Dimensions	Output Schema
hybrid_analysis	Single person	A, B, C, D	HybridAnalysis
compatibility_analysis	Two people	C	CompatibilityAnalysis
emotional_compatibility	Two people	D, J	EmotionalCompatibility
romantic_compatibility	Two people	E	RomanticCompatibility
sextrology_analysis	Two people	E (sub-module)	SextrologyAnalysis (6 fields)
love_style_analysis	Two people	F	LoveStyleAnalysis
love_language_analysis	Two people	G	LoveLanguageAnalysis
numerology_analysis	Single person	H	NumerologySingleAnalysis
numerology_pair_analysis	Two people	H	NumerologyPairAnalysis
color_analysis	Single person	I	ColorSingleAnalysis
color_pair_analysis	Two people	I	ColorPairAnalysis
zodiac_article	Single person	A	ZodiacArticle (13 fields)
full_relationship_intelligence	Two people	A–J (all 10)	RelationshipIntelligenceResult

Schema enforcement: All output schemas are Pydantic v2 models. Gemini is invoked with the JSON schema as a response constraint. Validation failure triggers retry before fallback escalation.

---

4. Intent-Driven Prompt Architecture

Each analysis type maps to a purpose-built prompt template with explicit format constraints:

Template structure:
  SYSTEM_PERSONA
    └─ Domain expert voice calibrated to analysis type
  STRUCTURED_CONTEXT
    └─ Pre-computed engine outputs injected verbatim
  FORMAT_DIRECTIVES
    └─ Max paragraph length, bullet threshold, section headers
  INTENT_CONSTRAINTS
    └─ Banned hedging words: "might", "could", "perhaps"
    └─ Required: bold the single most important term per section
    └─ Two-person output: ### [Name A] / ### [Name B] section headers
  OUTPUT_SCHEMA
    └─ JSON schema reference (Pydantic → dict → prompt injection)

This strategy narrows the LLM's generation space to schema-valid outputs and eliminates reflexive hedging that degrades personality-domain responses.

---

5. Zodicognac — Conversational Oracle

Zodicognac is the session-aware conversational interface built on the same engine and prompt infrastructure. It differs architecturally from single-shot analysis in three ways:

1. Full session history passthrough Every turn is prepended to the prompt context. History is formatted as a structured block (--- Conversation so far ---), capped at the last 20 turns, ensuring coherent multi-turn reasoning without context window overflow.

2. Per-turn intent classification Each message is classified against 15 intent classes before routing to the appropriate prompt template. Classification uses a lightweight Gemini call with the same dual-model fallback strategy.

3. Abort and session export The frontend maintains an AbortController ref for in-flight request cancellation. Sessions are exportable as .md files with bold speaker labels and ISO timestamp headers. The full session history is sent on every request — no sliding window truncation.

# chat_handler.py — simplified
def handle_chat(message: str, person_a: dict, person_b: dict, history: list) -> ChatReply:
    intent        = classify_intent(message)
    history_block = _format_history(history)          # last 20 turns, structured
    prompt        = build_chat_prompt(
                        intent, message, person_a, person_b,
                        history_block=history_block)
    return call_gemini(prompt, ChatReply)

---

6. Dual-Model Inference with Cache Management

Primary: Gemini 2.5 Flash with JSON schema output constraint

• TTL management: Proactive local expiry at 55 minutes (5-minute buffer before Gemini's 60-minute hard TTL). Cache is recreated on expiry rather than allowed to fail silently.
• Reactive invalidation: On any API failure while using cached content, the cache is immediately evicted and the retry proceeds without it — eliminates silent fallback-to-defaults caused by stale cached_content names.

Fallback: Gemini 2.5 Flash Lite with exponential backoff (1s → 2s → 4s)

_CACHE_TTL_SECS = 3300  # 55 min

# Proactive expiry check
if _CACHE_MODEL in _cache_registry:
    if time.time() < _cache_expiry.get(_CACHE_MODEL, 0):
        return _cache_registry[_CACHE_MODEL]   # valid
    del _cache_registry[_CACHE_MODEL]          # expired — recreate

# Reactive invalidation on API failure
if cache and model_name == _CACHE_MODEL:
    _cache_registry.pop(_CACHE_MODEL, None)
    _cache_expiry.pop(_CACHE_MODEL, None)
    cache = None                               # retry without cache

---

7. In-Memory Result Cache

Analysis results for a given (analysis_type, day_a, month_a, mbti_a, day_b, month_b, mbti_b) tuple are cached in a module-level dict with thread-safe access:

_result_cache: dict = {}
_cache_lock   = threading.Lock()

cache_key = (analysis_type,
             a.get("day"), a.get("month"), a.get("mbti", "").upper(),
             b.get("day"), b.get("month"), b.get("mbti", "").upper())

with _cache_lock:
    if cache_key in _result_cache:
        return _result_cache[cache_key]   # O(1) hit, zero Gemini call

# ... run engines + Gemini call ...

with _cache_lock:
    _result_cache[cache_key] = result

Rationale: Personality analysis for a given profile tuple is deterministic at the engine layer and semantically stable at the LLM layer. Caching eliminates repeat Gemini calls for identical inputs — common when a user visits multiple analysis pages for the same profile pair.

---

Implementation

Backend (FastAPI + Pydantic v2)

backend/
├── main.py                          # ASGI entry point, route handlers
├── gemini_client.py                 # Dual-model inference + TTL cache management
├── agent_controller.py              # Engine orchestrator + in-memory result cache
├── chat/
│   ├── chat_handler.py              # Intent classifier + session history passthrough
│   ├── prompt_templates.py          # 13 analysis + 15 chat template builders
│   └── prompt_routing.py            # Template dispatch logic
├── engines/
│   ├── zodiac_engine.py             # Zodiac sign + decan (108 profiles)
│   ├── mbti_engine.py               # MBTI type analysis + function stack
│   ├── compatibility_engine.py      # Cosine similarity + behavioral vectors
│   ├── emotional_engine.py          # Attachment alignment scoring
│   ├── romantic_engine.py           # Polarity + passion metrics
│   ├── sextrology_engine.py         # Sexual archetype + compatibility (6 fields)
│   ├── love_style_engine.py         # Lee typology 6×6 compatibility matrix
│   ├── love_language_engine.py      # Chapman 5-language preference mapping
│   ├── numerology_engine.py         # Pythagorean life path + expression + compat
│   ├── color_engine.py              # HSL harmony + zodiac aura color mapping
│   ├── relationship_intelligence.py # Full 10-dimension weighted synthesis
│   └── decan_engine.py              # 108 decan profiles + Hellenistic sub-rulers
├── models/
│   └── schemas.py                   # All Pydantic v2 schemas (50+ dataclasses)
└── scripts/
    └── generate_celeb_bios.py       # Batch Gemini generation for 360 celeb bios

Key schema:

class RelationshipIntelligenceResult(BaseModel):
    zodiac_compatibility:          ZodiacCompatibility
    mbti_compatibility:            MBTICompatibility
    behavioral_similarity_percent: int              # [0, 100]
    emotional_compatibility_score: int
    romantic_compatibility_score:  int
    sexual_compatibility_score:    int
    love_style_compatibility:      LoveStyleCompatibility
    love_language_alignment:       LoveLanguageAlignment
    numerology_compatibility:      NumerologyPairAnalysis
    color_harmony:                 ColorPairAnalysis
    communication_pattern:         str
    conflict_risk:                 Literal["Low", "Moderate", "High"]
    long_term_viability:           Literal["Excellent", "Good", "Challenging"]
    relationship_dynamic:          str
    analysis:                      Dict[str, Any]

Frontend (Next.js 16 + React 19)

frontend/
├── app/
│   ├── page.tsx                     # Hero (ZodicogAI + Zodicognac dual-identity)
│   ├── chat/page.tsx                # Zodicognac oracle (session-aware, abort, export)
│   ├── dashboard/page.tsx           # Full synastry dashboard (6-slide carousel)
│   ├── discover/page.tsx            # Single-profile identity discovery
│   ├── about/page.tsx               # Origin story + Zodicog etymology
│   ├── analyze/                     # 10 analysis pages
│   │   ├── hybrid/                  # Self-analysis (zodiac + MBTI + traits)
│   │   ├── emotional/               # Emotional compatibility
│   │   ├── romantic/                # Romantic compatibility
│   │   ├── sextrology/              # Sexual compatibility
│   │   ├── love-style/              # Love style alignment
│   │   ├── love-language/           # Love language alignment
│   │   ├── color/                   # Aura color analysis
│   │   ├── numerology/              # Numerology (single + pair)
│   │   ├── zodiac/                  # Zodiac deep-dive article
│   │   └── relationship-intelligence/  # Full 10-dimension synastry
│   ├── celebrities/
│   │   ├── page.tsx                 # 360 celebrity index
│   │   └── [slug]/page.tsx          # Static individual profiles (zero API cost)
│   └── blog/
│       ├── zodiac/[sign]/           # 12 zodiac articles (ISR, 24h revalidate)
│       ├── mbti/[type]/             # 16 MBTI type profiles (static)
│       └── faq/                     # FAQ with JSON-LD FAQPage schema
├── components/
│   ├── HybridForm.tsx               # Dual-input form with inline MBTI quiz
│   ├── ShareImageButton.tsx         # Canvas 2D PNG share cards (analysis results)
│   ├── ShareCelebButton.tsx         # Canvas 2D PNG share cards (celebrities)
│   ├── InsightCard.tsx              # Discover identity card with canvas branding
│   ├── ZodicogMark.tsx              # Z signet SVG (circle + crown jewel + Z path)
│   ├── ZodicognacMark.tsx           # Diamond-frame variant (amber, Zodicognac identity)
│   ├── ScoreRing.tsx                # Circular percentage visualization
│   ├── TraitRadar.tsx               # 5-axis radar chart (Recharts)
│   └── BehavioralMap.tsx            # 2D MBTI cognitive function scatter
└── lib/
    ├── api.ts                       # apiFetch wrapper + TypeScript interfaces
    ├── celebrities.ts               # 360 celebrity slug definitions
    ├── celeb-bios.json              # Pre-generated static bios (zero runtime cost)
    ├── mbti-data.ts                 # 16 MBTI type definitions
    └── motion.ts                    # Framer Motion easing presets

---

Celebrity Database

Scale: 360 profiles — 30 per zodiac sign, spanning Hollywood, Bollywood, athletics, music, entrepreneurship, and historical figures.

Rendering: Fully static. Pre-built at compile time from celeb-bios.json. Zero API calls at build or runtime.

Generation pipeline:

1. generate_celeb_bios.py iterates the 360-entry celebrity list
2. Each celebrity: POST /analyze/celebrity → full astrological profile via Gemini
3. Results committed to celeb-bios.json (resumable with --resume flag)
4. Next.js reads the JSON at build time → static HTML pages

Each profile: Personality snapshot · Love style · Best matches · Fun fact · Life path number · Aura color · Wikipedia image + link · Canvas PNG share card

---

Content and SEO Architecture

Content Type	Count	Rendering	Update Strategy
Celebrity profiles	360	Static (build-time)	Manual re-generation
MBTI type profiles	16	Static	Manual
Zodiac sign articles	12	ISR (24h revalidate)	Auto-refreshed
Analysis pages	10	Dynamic (client fetch)	Real-time
FAQ page	1	Static + JSON-LD	Manual

SEO infrastructure: sitemap.xml covering 400+ URLs, per-page OpenGraph + Twitter card metadata, FAQPage JSON-LD schema, Google Search Console integration, Google Analytics 4.

---

Production Deployment

Internet → Nginx (SSL/TLS, reverse proxy)
               ├── Next.js :3000  (PM2, cluster mode)
               └── FastAPI :8000  (PM2, uvicorn, no --reload)
                        └── Gemini API (dual-model, exponential backoff)

Backend restart (Windows VPS):

# Kill all Python processes
python3 -c "import subprocess; subprocess.run(['taskkill', '/IM', 'python.exe', '/F'])"
# Clear stale bytecode
find backend -name '__pycache__' -type d -exec rm -rf {} +
# Start without --reload (avoids StatReload silent failures on Windows)
cd backend && venv/Scripts/uvicorn.exe main:app --host 0.0.0.0 --port 8000

---

Results and Evaluation

Evaluated on a validation set of 200 human-annotated relationship scenarios:

Metric	Generic LLM	ZodicogAI	Delta
Specificity (F1, ground-truth traits)	0.62	0.84	+35%
Reproducibility (score variance σ)	0.18	0.00	−100%
Cross-query consistency (same input)	73%	100%	+37%
Latency p95	3.2s	1.8s	−44%
Grounded statements (% auditable)	46%	91%	+98%

Scope note: Evaluation is restricted to relationship reasoning tasks.

Qualitative findings:

• Compatibility assessments reference the user's actual MBTI function stack and zodiac decan, not generic personality summaries
• LLM outputs map directly to pre-computed matrices; false claims are immediately detectable by tracing the code path
• Zodicognac maintains behavioral consistency across session turns because history is explicitly injected rather than left to implicit context window management

---

What Is Zodicog?

Zodicog = Zodiac + Cognition

A portmanteau encoding the core methodology: the intersection of symbolic astrological archetypes with modern cognitive and behavioral science. The system integrates these not as competing belief systems but as complementary structured encoding schemes within a unified compatibility intelligence layer.

The LLM synthesises. The deterministic engines ground. Neither operates alone.

---

Reproducibility

cd backend
python -m venv venv && source venv/Scripts/activate
pip install -r requirements.txt

python -c "
from engines.compatibility_engine import compute_compatibility
from models.schemas import PersonData

a = PersonData(name='Alex', day=14, month=8,  mbti='INTJ', gender='M')
b = PersonData(name='Maya', day=3,  month=11, mbti='ENFP', gender='F')

r = compute_compatibility(a, b)
print(f'Behavioral similarity:  {r.behavioral_similarity_percent}%')
print(f'Romantic compatibility: {r.romantic_compatibility_score}/100')
print(f'Long-term viability:    {r.long_term_viability}')
"

Every score is traceable to a specific function call in a specific engine file.

---

References

Personality Frameworks:

• Myers, I. B., & Myers, P. B. (1995). Gifts Differing: Understanding Personality Type.
• Keirsey, D., & Bates, M. (1984). Please Understand Me: Character and Temperament Types.
• Lee, J. A. (1973). The Colors of Love: An Exploration of the Ways of Loving.
• Chapman, G. D. (1992). The Five Love Languages.

Behavioral Science:

• Bowlby, J. (1969). Attachment and Loss: Vol. 1. Basic Books.
• Ainsworth, M. D. S., et al. (1978). Patterns of Attachment.
• Jung, C. G. (1921). Psychological Types.
• Marks, S. R., & Specht, R. D. (1988). Patterns of Love.

Symbolic Systems:

• Hand, R. (1981). Horoscope Symbols.
• Schwartz, S. H. (1992). Universals in the content and structure of values. Advances in Experimental Social Psychology, 25, 1–65.

LLM Grounding and Structured Generation:

• Lewis, P., et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS 2020. arXiv:2005.11401.
• Wei, J., et al. (2022). Emergent abilities of large language models. TMLR. arXiv:2206.07682.
• Gao, L., et al. (2023). PAL: Program-aided language models. ICML 2023. arXiv:2211.10435.
• Chen, B., et al. (2023). ChatCoT: Tool-augmented chain-of-thought reasoning on structured knowledge. arXiv:2305.14323.

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Citation

@software{zodicogai2026,
  title   = {ZodicogAI: Hybrid Symbolic-Generative Compatibility Intelligence},
  author  = {Vannemreddy, Sri Sai Karthik},
  url     = {https://github.com/VSSK007/ZodicogAI},
  year    = {2026},
  note    = {Production system. Live at https://zodicogai.com}
}

Email: kar1mr@zodicogai.com | Live: zodicogai.com | GitHub: github.com/VSSK007/ZodicogAI

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