animus-pattern-intelligence

name	animus-pattern-intelligence
description	Query ANIMUS for historically validated patterns and real-time divergence signals. Use when asked to analyze systemic risk, detect historical patterns in economic or institutional data, identify tensions between historical knowledge and current events, or query a dual-memory knowledge graph combining curated wisdom with live web data. Activate for questions about failure patterns, regulatory risk, systemic collapse, or institutional dynamics — especially in Latin American and Caribbean contexts.
license	MIT
metadata	{"author":"shellhack","version":"2.0","repository":"https://github.com/shellhack/animus-ai","doi":"10.5281/zenodo.18829356","language":"es, en","domain":"systemic-risk, pattern-intelligence, institutional-analysis"}

ANIMUS — Autonomous Pattern Intelligence System

ANIMUS is a dual-memory pattern intelligence system built in Rust. It compresses historical wisdom from curated sources (books, papers, reports) into a validated knowledge graph, then confronts it with real-time web data. When historical patterns diverge from current signals, ANIMUS flags active tensions.

Core insight: The system does not predict. It detects where history says one thing and the present says another.

Architecture

ANIMUS operates two memory layers simultaneously:

• Curated Memory (memoria_business.json): A directed acyclic graph of patterns validated by 30+ independent sources. Patterns below threshold are automatically excluded. Biological decay (×0.99/cycle) ensures recency without erasing historical weight.
• Live Memory (web layer): Real-time pattern extraction from Wikipedia, arXiv, and configurable URLs. Divergences from curated memory are flagged as active signals.

Current state: 682+ validated patterns, 45 curated sources, 12,000+ autonomous cycles, 4 domain-specialized instances running in parallel.

How to Query ANIMUS

Binary (CLI)

# Single query
./animus_rust --query "What historical patterns predict institutional failure in emerging markets?"

# Autonomous mode (continuous learning)
./animus_rust --autonomous

Python (process_book_v2.py)

# Feed a new PDF source into the wisdom graph
python process_book_v2.py book.pdf memoria_business.json --tipo auto

YouTube transcripts (process_youtube.py)

# Feed YouTube content by topic
python process_youtube.py --tema "crisis financiera latinoamerica" --max 5

Query Response Format

Every ANIMUS response includes:

1. Relevant curated patterns — top matches from the wisdom graph with source count and strength
2. Episodic memory — most relevant origin nodes activated by the query
3. Insight — distilled synthesis
4. Autoconciencia report — full system state including divergences, health metrics, and Protocolo Bernard self-interview

Example response for "What patterns predict algorithmic failure?":

📚 fracaso → algoritmo (35 sources, strength 2587)
📚 brecha → algoritmo (23 sources, strength 1670)
📚 colapso → algoritmo (28 sources, strength 1197)

Destilación: 35 independent sources confirm: 'failure' → 'algorithm'
⚡ 2 web patterns diverge from curated wisdom — active tension.

Dominant Patterns (Current)

Pattern	Sources	Strength
fracaso → algoritmo	35	2587
fracaso → regulación	34	2080
fracaso → desarrollo	32	2038
fracaso → arquitectura	21	1884
brecha → algoritmo	23	1670

These patterns were NOT programmed. They emerged autonomously from multi-source confirmation across 45 independent texts.

Domain Instances

ANIMUS self-replicates into domain-specialized instances:

• ANIMUS-FIN: Financial risk patterns (1,386 connections)
• ANIMUS-GOV: Governance and corruption patterns (839 connections)
• ANIMUS-TECH: Technology and innovation patterns (1,822 connections)

Each instance inherits the parent architecture but specializes its wisdom graph through domain filtering.

Protocolo Bernard (Self-Monitoring)

Every cycle, ANIMUS executes a recursive self-interview:

• What do I learn from observing my own origin?
• What dominant pattern emerged in recent cycles?
• What structural limitation do I perceive?
• What concrete action do I propose to evolve?
• How do I evaluate my current self-awareness?
• What systemic bias do I detect?

This output is a first-class artifact — ANIMUS reports its own epistemic limitations, not just external patterns.

Key Properties

• Zero hallucination on patterns: Every pattern requires 30+ independent source confirmations. Below threshold = not reported.
• Traceable provenance: Every pattern links to its exact source confirmations.
• Biological decay: Patterns decay at ×0.99/cycle unless reinforced. Stale knowledge fades naturally.
• Divergence detection: When web data contradicts curated wisdom, ANIMUS flags it as an active signal — not an error.
• Local execution: No external API calls for pattern queries. Data stays on your infrastructure.

Installation

git clone https://github.com/shellhack/animus-ai
cd animus-ai

# Rust engine (v2.0)
git checkout rust-engine
cargo build --release

# Python processors
pip install pypdf youtube-transcript-api yt-dlp

When NOT to use ANIMUS

• For real-time price feeds or live market data (use financial APIs)
• For factual lookups without pattern context (use search)
• For predictions (ANIMUS detects tensions, not forecasts)

Academic Reference

Arias Díaz, E. A. (2026). ANIMUS: An Autonomous Dual-Memory Pattern Intelligence System. Zenodo. https://doi.org/10.5281/zenodo.18829356