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Noble VisionAI Research Lab
Autonomous AI experimentation powered by 144 specialized agents across 12 divisions. Combining Karpathy's AutoResearch methodology with enterprise-grade agent orchestration.
Total Agents
144
Active Divisions
12
Running Experiments
3
Success Rate
87%
Divisions
Research & Discovery
12 agents
7 / 12 active
NLP
12 agents
7 / 12 active
Computer Vision
12 agents
7 / 12 active
Reinforcement Learning
12 agents
7 / 12 active
Data Engineering
12 agents
7 / 12 active
Security & Compliance
12 agents
7 / 12 active
MLOps & Deployment
12 agents
7 / 12 active
Knowledge Graphs
12 agents
7 / 12 active
Generative AI
12 agents
7 / 12 active
Autonomous Agents
12 agents
7 / 12 active
Analytics & Observability
12 agents
7 / 12 active
Human-AI Collaboration
12 agents
7 / 12 active
Recent Activity
Proposal submitted — multi-stage distillation from 70B teacher to 7B student model
Knowledge Distillation Pipeline
Epoch 14/50 — learned PE memory footprint 3x higher than RoPE at 128K
Positional Encoding Variants
Proposal submitted — reward model calibration using temperature scaling and ensemble disagreement
Reward Model Calibration
Epoch 22/50 — cross-lingual alignment loss stabilizing across all 4 languages
Cross-Lingual Transfer Learning
Epoch 34/50 — curvature-aware schedule showing 11% faster convergence
Dynamic Learning Rate Scheduling
Model Performance
Avg val_bpb
0.98 / 2.0
Accuracy
87 / 100
Throughput
1,247 tok/s