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Experiments with interactive theorem provers, LLMs and formal systems

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LeanGPT

Brainstorming an Abstract & Features

The idea is to use LLM agents<->Theorem provers pipeline for:

  • AI-assisted Proof Generation & Interpretation:

    • allow users to input natural language proofs, which are then processed by a Middleware/Backend service that interfaces the AI agent with the Theorem Prover agent.
    • improve the interpretability of the Theorem Prover's results and translated them back to natural language
    • provide proof-assistance, ai-assisted debugging
    • to investigate further: would something like AutoGPT be useful to partially automate [1] the proof generation / drafting&sketching&refining proof feeds to be passed&validated to the Theorem Prover?
  • AI-assisted Math Proof Search Engine:

    • allows users to write natural language queries to look for theorems & proofs
    • provide interpretability for the search results
    • provide autocomplete feature

[1] the word automate is used with a grain of salt here because 1) LLMs are currently not quite good at math, they are trained on informal mathematical data and tend to produce a high rate of erroneous proofs & math results due to their reasoning limitations when dealing with complex propositional logic & formal languages/systems 2) the concept of automation finds its theoretical limitations in the case of automated theorem prover using a formal system like Peano Arithmetic due to goedel incompleteness theorems - and if i'm talking rubbish feel to open a PR. long story short by automation here I mean setting a goal and repeating the procedure of suggesting new informal problem solving pathways until said goal is reached.

Sequence Diagram

sequenceDiagram
  participant User as user
  participant AI Agent as ai-agent
  participant Middleware as middleware
  participant Theorem Prover as theorem-prover
sequenceDiagram
  user ->> ai-agent: User Input
  ai-agent -->> middleware: User Input
  middleware ->> middleware: Relevant Information
  middleware ->> theorem-prover: Encoded Mathematical Definitions, Theorems & Proof-Search Task
  theorem-prover -->> middleware: Perform Proof-Search & Reasoning
  middleware -->> middleware: Formatted Proof
  middleware -->> ai-agent: Response with Proof
  ai-agent -->> user: Generated Response

Just for starters, the AI agent is GPT3, the Theorem Prover entity are COQ and the Lean theorem prover. The backend/middleware entity might be a basic flask API (TBD), but it could be replaced later with Langchain to experiment with interoperability.

State Machine Diagram

State Diagram

Neat resources