From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
By: Jordi de la Torre
Potential Business Impact:
AI learns to build and use its own tools.
We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles to guide future implementations of interactive AI systems that integrate symbolic programming with neural language generation.
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