AI Research

Competition AI/ML Models

Advanced Neural Architectures for Specialized Challenges

Our competition models represent cutting-edge AI research, from ancient language translation to abstract reasoning challenges. Each model showcases innovative architectures and training methodologies developed by AutomataNexus.

Research-Driven

Competition models pushing the boundaries of AI capabilities in specialized domains.

State-of-the-Art

Implementing cutting-edge architectures and training methodologies.

Benchmarked

Rigorously evaluated against industry-standard metrics and challenges.

Ensemble Approaches

Combining multiple specialized models for superior performance.

Our Models

Specialized neural architectures for competition-grade AI challenges

NexusSage

Neural Machine Translation

PyTorch + Transformers

Advanced dual-architecture system for translating 4,000-year-old Old Assyrian cuneiform text to modern English.

20.3
BLEU Score
48.7
chrF++
647M
Parameters

Key Features

  • Dual transformer architecture (MT5/mBART/NLLB + Custom)
  • Character-level tokenization for morphological complexity
  • Determinative-aware processing for semantic markers
  • Ensemble strategy for best-in-class performance
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Olympus AGI2

Abstract Reasoning Ensemble

PyTorch

Multi-model ensemble system for the ARC (Abstract Reasoning Corpus) challenge, targeting human-level pattern recognition.

64.5%
Peak Accuracy
98.5%
Specialist Peak
8.4M
Parameters

Key Features

  • 5 specialist models: MINERVA, ATLAS, IRIS, CHRONOS, PROMETHEUS
  • Grid-aware attention with learnable position embeddings
  • Mega-scale curriculum learning (batch size 2048)
  • Decision fusion with meta-learning
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Mnemosyne

Alzheimer's Detection System

PyTorch

Clinical-grade multi-modal ensemble for early-stage Alzheimer's disease detection using MRI imaging and genomic biomarker analysis.

>85%
Target Accuracy
87.7%
Best Single
100M+
Parameters

Key Features

  • 4 MRI experts: DenseNet201, EfficientNet-B4, ResNet152, AttentionCNN
  • 6 Genomic experts for SNP/chromosome analysis
  • Meta-learning ensemble with attention mechanism
  • Grad-CAM explainability for clinical interpretability
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