Overview

A neuro-symbolic Small Language Model (SLM) for translating natural language into Lean 4 formal specifications, enabling verification-native autonomy for mission-critical domains such as finance, semiconductors, and robotics.

Details

Project MEDHA proposes the development of a Sovereign Autoformalization Model (SAM), a domain-specific neuro-symbolic SLM optimized for converting natural language mathematics, policies, and specifications into formally verified Lean 4 code.

Unlike probabilistic LLMs, MEDHA is designed around deterministic verification through a generation–verification loop, where a symbolic theorem prover continuously validates and repairs generated formalizations until correctness is achieved.

The initiative aims to establish India’s sovereign infrastructure for verification-native AI systems across mission-critical sectors including quantitative finance, semiconductor design, robotics, and scientific computing.

Current work

• Building a high-speed autoformalization pipeline for Lean 4 theorem proving.
• Developing neural-symbolic generation and verification loops using compiler feedback.
• Extending Mathlib into domain-specific libraries such as FinanceLib and PhysicsLib.
• Formal verification of trading algorithms and regulatory compliance systems.
• Verification-native architectures for robotics and semiconductor correctness.
• Exploring artificial mathematician systems for education and scientific reasoning.

Research themes

• Autoformalisation and theorem proving
• Neuro-symbolic AI systems
• Verifiable superintelligence
• Formal methods for finance
• Lean 4 and dependent type theory
• Deterministic AI infrastructure
• Sovereign AI systems

Vision

MEDHA seeks to move beyond stochastic AI generation toward formally verified intelligence systems capable of operating safely in high-stakes environments. By combining mathematical logic, formal verification, and domain-specific reasoning, the project aims to create foundational infrastructure for trustworthy autonomous systems.