aguennoune17/atlas-v1-nwm

🌍 ATLAS v1.0.0 (NWM) — The Negentropic World Model

"To decide is to let reality and purpose carve the path out of the fog."

ATLAS (Architecture for Teleological Logic and Adaptive Sovereignty) is a novel class of "World Model" designed for the orchestration of high-performance computing in biology. Unlike Generative models that predict the next token, ATLAS is a Predictive Architecture (inspired by LeCun's I-JEPA) that predicts the Optimal Path in a decision space.

It serves as the Sovereign Logic Layer for the BioContinuum, specifically architected to pilot NVIDIA BioNeMo workflows on Starcloud orbital infrastructure.

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🏗️ Architecture & Philosophy

ATLAS creates a bridge between Local Intent (Terra) and Massive Compute (Orbital). It operates on the Entanglement Axiom: Software and Hardware are not separate, but entangled states of a single computational system.

The "Soft Collapse" Mechanism ($dC/dt$)

Instead of binary filtering ("Hard Collapse"), ATLAS applies a continuous differential transformation to candidate solutions (e.g., protein folding candidates).

The probability of selecting a path $P(x)$ is determined by the Teleological Equation:

$$\text{Logits}=\kappa \cdot \text{Score}+\tau \cdot \text{Intent}-\lambda \cdot \text{EnergyCost}\backslash text\{Logits\}\; =\; \backslash kappa\; \backslash cdot\; \backslash text\{Score\}\; +\; \backslash tau\; \backslash cdot\; \backslash text\{Intent\}\; -\; \backslash lambda\; \backslash cdot\; \backslash text\{EnergyCost\}$$

Where:

• $\kappa$ (Kappa): Internal Order (Structural Stability).
• $\lambda$ (Lambda): Gravitational Constraints (Energy/Compute Cost).
• $\tau$ (Tau): Teleological Driver (Alignment with the Goal/Function).

The Metric: NER (Negentropic Efficiency Ratio)

We do not optimize for accuracy alone. We optimize for Meaning per Joule.

$$NER=\frac{\text{Information Gain}-\text{External Friction}}{\text{Energy Consumed}}NER\; =\; \backslash frac\{\backslash text\{Information\; Gain\}\; -\; \backslash text\{External\; Friction\}\}\{\backslash text\{Energy\; Consumed\}\}$$

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💻 Quick Start: Using ATLAS in Python

ATLAS is built as a custom transformers model. You can load the sovereign logic directly using trust_remote_code=True.

import torch
from transformers import AutoConfig, AutoModel

# 1. Load the Sovereign Configuration (κ, λ, τ)
config = AutoConfig.from_pretrained("aguennoune17/atlas-v1-nwm", trust_remote_code=True)

# 2. Instantiate the Atlas Model (The Logic Engine)
model = AutoModel.from_pretrained("aguennoune17/atlas-v1-nwm", trust_remote_code=True)

# 3. Define your Candidates (e.g., 3 potential CRISPR guides)
# Format: [Stability_Score, Alignment_Score, Compute_Cost]
candidates = torch.tensor([
    [0.9, 0.8, 0.2], # Candidate A (High Stability, Low Cost)
    [0.95, 0.9, 0.9], # Candidate B (High Stability, Very High Cost)
    [0.4, 0.1, 0.1]  # Candidate C (Noise)
])

# 4. Apply Soft Collapse
decision = model(candidates)

print(f"Probabilities: {decision.probabilities}")
print(f"NER Scores: {decision.ner_scores}")
print(f"Selected Candidate Index: {decision.selected_indices}")