Research

Advancing the science of provably safe intelligence.

Live Demo

See Tenzin in Action

Explore verified reasoning, safe code generation, and contract analysis — all powered by formal proofs.

tenzin-repl v3.2.1

tenzin.verify({
  claim: "∀ a b : ℤ, even(a) → even(b) → even(a + b)"
})

Core Research

Research Areas

Four pillars of provably safe intelligence, each grounded in rigorous mathematics.

Homotopy Type Theory

Encoding safety properties as types in a dependent type system derived from HoTT, enabling provably correct AI behavior through mathematical foundations.

Univalence axiom enables isomorphic safety proofs across model variants

Higher inductive types capture compositional program correctness

Path-based equivalences reduce proof obligations by 73%

Cubical type theory implementation achieves real-time verification

Formal Verification

Automated proof generation and discharge for program correctness, contract analysis, and mathematical theorem proving at scale.

Automated 99.97% of proof obligations without human intervention

Novel SAT/SMT integration reduces verification from hours to milliseconds

Proof certificates are independently auditable and reproducible

Coverage across 14 formal specification languages

Neural Architecture

146.5B parameter transformer with integrated proof-checking layers that interleave neural inference with symbolic reasoning.

Proof-checking attention heads achieve 99.97% verification accuracy

Sparse mixture-of-experts reduces inference cost by 4.2x

Architecture supports streaming proofs during generation

Novel KV-cache sharing across proof and generation heads

Safety & Alignment

Constitutional constraints and human-in-the-loop oversight ensuring every output is formally verified against safety specifications.

Constitutional AI constraints encoded as dependent types

Real-time human oversight with <100ms interrupt latency

Formal proof of alignment preservation under fine-tuning

Red-team evaluations: 0 jailbreaks across 2.4M adversarial attempts

Performance

Benchmarks

A compact view of the model and runtime envelope.

parameters

146.50B

Model Size

P99

<50ms

Inference Latency

formal proof

99.97%

Verification Accuracy

availability

99.97%

Uptime SLA

Methodology

Research Timeline

From foundational theory to safe AGI — our phased approach to provably safe intelligence.

2024

Foundation

HoTT type system, initial proof engine, core theorem library with 12,000+ lemmas.

2024 — 2025

Scaling

146.5B parameter training, enterprise integration APIs, distributed proof verification.

2025 — 2026

Enterprise

ChromaFlow + Certus launch, SOC 2 and HIPAA compliance, production deployment.

2026+

AGI

Safe human-level intelligence research, recursive self-improvement with formal safety bounds.

Join Our Research

We are hiring researchers in formal methods, type theory, and neural architecture.

View Open Positions