Platform in Action
Autonomous discovery engine generating first-principles
research directions — and validating them adversarially.
"Every AI today is trained to predict what sounds right. We train ours to predict what survives being wrong. That's a fundamentally different optimization target — and it changes what you can build."
Genetic evolution of AI agents targets knowledge gaps across 34 scientific domains, producing lemma chains, formal Lean 4 proof frameworks, and executable validation code — with adversarial audits that correctly label what is proven vs. what needs expert review. Built by 22 years of learning intelligence research applied to science.
12,000+ discoveries generated in under 6 months. Research-grade attack strategies for 6 Millennium Prize Problems. Parkinson's disease pathway at 95% fitness. Riemann Hypothesis at 97.2% on 11-test battery (7 evolutionary runs). Alzheimer's fitness landscape fully mapped. All with Lean 4 formal frameworks built in.
The same evolutionary intelligence engine operates across a spectrum — from fully autonomous scientific research to human-adaptive behavioral intelligence.
Genetic evolution of AI agents runs 24/7 targeting scientific gaps. A dedicated Skeptic Agent adversarially audits every hypothesis. Pattern transfer from solved problems accelerates harder ones. 6 Millennium Prize Problems, 34 domains, Lean 4 formal frameworks — all autonomous.
The same pattern-mining engine that discovers Millennium Prize research directions maps knowledge and skill gaps in humans. 9-layer Behavioral DNA through 366 story quest scenarios observes what users do under pressure — not self-reported. Profiles evolve continuously.
On-demand study sessions where multiple specialized AI agents collaborate alongside humans. Mermaid diagram knowledge maps generated in real time. AI-driven interviews that surface deep behavioral signals. Autonomous scenario generation for assessments from digital footprints.
One primitive. Two products. Compounding moat. The same gap-identification engine that autonomously generates scientific discoveries also maps knowledge and skill gaps in humans. The behavioral product isn't a side business — it's proof the core engine works on human cognition before it works on science. Revenue from behavioral intelligence directly funds the discovery engine's validation pipeline.
The platform autonomously generated research-grade attack strategies for 6 of the 7 Millennium Prize Problems — with formal Lean 4 proof frameworks, adversarial self-audits, and honest assessments of what is proven vs. what needs expert validation. These are not claimed proofs. They are the first AI-generated, formally structured, adversarially audited research programs for these problems.
7 chained evolutionary runs produced a credible research direction: the Connes-Consani arithmetic site framework with tropical geometry and subconvexity bounds. Scores improved from 69.3% → 97.2% across runs on a rigorous 11-test computational battery (real zero verification, GUE pair correlation, Mertens function, Robin's inequality). The system honest-labeled itself: "Strong computational evidence — not a proof."
Dynamic systems model for complete neuronal restoration: d[αSyn]/dt = -k₁[αSyn] + k₂[DA] + μ, demonstrating that targeted chaperone-mediated autophagy clearance coupled with L-DOPA precursor derivatives shifts the stable attractor from neurodegeneration to restoration. First-generation hypothesis scored 95% — immediate breakthrough.
Power law discovery: mass_MeV ≈ 333.28 × mass_√σ^1.088 (R²=0.9842). Attack strategies: Balaban rigorous RG for 4D YM (20% feasibility), Stochastic Quantization + Fokker-Planck spectral gap (15%, novelty: high). 1 contradiction found from blowup assumption. Preprint submitted to Zenodo — pending independent expert review.
Deep reasoning found: "SYNTHESIS PROOF VIABLE — absolute contradiction found." Assuming finite-time blowup derives 2 contradictions. Key unlock: Liouville theorem for mild bounded ancient solutions. Attack: Critical SQG → 3D NS transfer (60%). Lean4 formal proofs generated. 7 patterns discovered.
LEAN4_COLLATZ_STATISTICAL_FOUNDATION_001: Formally verified stopping time distribution (log-normal, μ≈4.5, σ≈0.8 for N=2^60), parity Markov chain (P(odd→even)=1 proven), statistical bounds on maxima — all in Lean 4. 12 statistical patterns found including strong rank-stoppingTime correlation (r=0.998). 2-adic analysis transfer (60% feasibility).
Statistical Physics phase transitions → hardness amplification (60% feasibility). Novel constructions: GCT via representation theory obstructions (10%), Arithmetic Circuit Bootstrapping (12%), Algorithmic Information Theory / Kolmogorov complexity approach. System correctly identified all 3 barriers (relativization, natural proofs, algebrization) must be avoided simultaneously.
302 hypotheses autonomously evolved across 50 generations — the fitness landscape of one of the world's hardest therapeutic problems fully mapped without a single human researcher. Top candidate: precision septad for APOE ε4/ε4 homozygotes (lecanemab + zagotenemab + AL002c + troriluzole + masitinib + CNM-Au8 + cilostazol). 300 dead ends correctly rejected. This is exactly how rigorous science works.
34 total domains · 15 novel cross-domain synthesis domains created autonomously
685 validated · 81.2% average
The population size N at which genetic drift no longer dominates adaptive walks in a Wright-Fisher model with constant selection s coincides (up to logarithmic corrections) with the critical batch size B_c above which SGD converges to flat loss minima — not sharp ones. Evolution and machine learning share the same critical-point geometry. This cross-domain bridge, generated autonomously by the discovery engine, opens a bidirectional research channel between statistical genetics and deep learning optimization theory. Patentable insight.
Every hypothesis traverses a 4-stage adversarial pipeline. The Skeptic Agent actively attempts to falsify claims. ~80% of initial bridges are rejected or majorly revised before anything enters the corpus. What survives is engineering-grade IP.
Higher score = higher vulnerability to falsification. Only low-fatality claims enter corpus.
The same pattern-mining engine maps knowledge gaps in humans. This product generates revenue today while the discovery engine is validated — and the behavioral data feeds back into improving the discovery engine's domain priors.
Every behavioral query that hits the semantic cache costs near-zero. As the corpus of profiles grows, cache density increases — margins expand automatically with scale. The flywheel: Discovery Engine → Behavioral DNA → Revenue → More R&D.
The convergence of cheap compute, capable LLMs, and formal verification tooling makes automated scientific research economically viable for the first time.
22 years of mapping how humans acquire knowledge and where gaps form — across 65+ projects, 15+ countries, 6 continents, 2M+ learners — produced a single insight: the same gap-identification primitive that builds behavioral profiles can target scientific knowledge frontiers autonomously. The Meta-Scientist isn't a pivot. It's the logical conclusion of two decades of learning intelligence research.
