Llms Forget. Redprint Remembers

We’ve been experimenting with a symbolic reasoning layer that sits beneath local LLMs, aimed at compressing their context demands by shifting some of the burden into structured memory and logic. The core idea: instead of infinitely scaling GPUs and tokens, we scaffold a learning loop that binds action to outcome, reflection to memory, and memory to improvement.

The architecture includes:

* A lightweight Prolog or Datalog engine to track symbolic execution paths

* Vector and symbolic memory fusion to keep both nuance and structure

* Outcome tests that update a learning score over time, enabling the agent to refine future actions

* Curiosity modules that bias the agent toward resolving ambiguity or closing feedback loops

One example: we had an agent running a simple multi-step task loop with an objective scoring function. First run: 0 percent success. But as outcome chains were logged and the reasoning engine updated its knowledge base, the same model climbed into the 70 percent range over 60 runs. No retraining, no fine-tuning, just structured feedback and symbolic state retention.

Still early, but the goal is not just to build a better chatbot or prompt wrapper. We’re aiming for something more like a persistent local intelligence that reasons through problems, remembers its missteps, and adapts without external retraining.