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We believe the future of AI must be intertwined with the future of our planet. That's why profit from AegisMind directly funds OceanSparx's mission to develop cost-competitive green energy solutions.
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AegisMind orchestrates five frontier models simultaneously — each independently generating hypotheses, then critiquing each other in adversarial debate before formal verification
GPT, Claude, Gemini, Grok, and Mistral run in parallel. No single model's biases dominate the output.
Models critique each other's hypotheses. Only survivors of adversarial pressure are kept. Debate scores reflect survival strength.
Papers from 15+ domains — physics, biology, CS, climate, economics — embedded and searched for non-obvious cross-field connections.
| Capability | Single-model AI | AegisMind |
|---|---|---|
| Model Coverage | 1 provider | 5 providers (all frontier) |
| Adversarial Debate | ✗ | ✓ |
| Formal Verification (Z3) | ✗ | ✓ |
| Cross-domain paper search | ✗ | ✓ |
| Experimental Validation Package | ✗ | Full protocol + cost model |
| Meta-Learning | ✗ | Improves with every run |
Revolutionary technology that makes green hydrogen economically viable
Deployed on water reservoirs, reducing evaporation by up to 70% in arid climates
On-site electricity production feeds electrolysis units, maximising efficiency
Uses only a fraction of water saved from evaporation reduction
Up to 70% reduction in water loss from reservoirs
Shading effect improves water quality
Only uses small percentage of saved water for hydrogen production
Reduces transmission losses, increases efficiency
Cooler panel temperatures = better performance
Free cooling, reduced land costs, co-benefits
Research discovery today. Clean energy tomorrow.
John Goodman — AegisMind Research · May 2026
A 42-phase empirical study characterising inter-precision Linear Mode Connectivity (LMC) barriers across FP32, BF16, FP16, and INT8 for models from 1M to 124M parameters. Identifies a model-size scaling law (FP32↔BF16 barrier ∝ params−0.85, R² = 0.98) placing ~10M parameters as the practical basin-separator for mixed-precision training, and an isosceles precision triangle extended to an irregular four-vertex INT8 tetrahedron replicated across transformer, LSTM, and ResNet architectures.
Applied to multi-target drug discovery: a target-conditioned surrogate trained on 15,834 Vina docking scores achieves Spearman ρ = 0.827 across six therapeutic targets (LINGO1, PCSK9, KPC3, APEX1, MSH3, CREBBP). Nash equilibrium drug combination optimisation converges on EPTIFIBATIDE as a dual KPC-3 / MSH3 candidate — independently confirmed by Vina docking, Nash synergy analysis, and surrogate Bayesian Optimisation.
DOI: 10.5281/zenodo.20363636 · All 42 phases on Google v6e-8 TPUs (us-east5-b, us-east1-d, us-central1-b, europe-west4-a)
Point the discovery engine at your domain's literature. Get ranked hypotheses with full experimental validation packages.