NeuroSplit: Decoupling Memory and Reasoning to Build a New Paradigm for Explainable Large Language Models

NeuroSplit decouples the memory retrieval and reasoning processes of large language models (LLMs) using structured tags {memory} and {reason}, significantly improving model interpretability, reducing hallucinations, and enabling step-by-step debugging. The project employs LoRA and prompt tuning techniques, offering new insights for building more transparent and reliable AI systems.

The internal working mechanism of current large language models (LLMs) is vague, with two core issues: insufficient interpretability and frequent hallucinations. Traditional fine-tuning methods train memory and reasoning abilities in a mixed way, leading the model to over-reason when recalling facts accurately or rely on incorrect memory during logical deduction, reducing reliability and making debugging difficult.

NeuroSplit proposes an explicit structured tag scheme: using {memory} to identify the process of the model retrieving factual knowledge, and {reason} to mark steps of logical deduction based on facts. This separation makes the internal operation of the model transparent and visible, allowing developers to identify memory and reasoning stages and enabling targeted optimization.

NeuroSplit uses a Parameter-Efficient Fine-Tuning (PEFT) tech stack, based on LoRA (Low-Rank Adaptation) and prompt tuning. LoRA injects new behavioral patterns without modifying the original model parameters; prompt tuning enables the model to learn to recognize {memory} and {reason} tags. The project is built on HuggingFace Transformers, supports mainstream open-source models, and can be reproduced by ordinary developers on consumer-grade GPUs.

Tests show that NeuroSplit significantly reduces the incidence of hallucinations; the forced separation makes the model more inclined to retrieve knowledge accurately rather than fabricate content. When the model makes mistakes, developers can quickly locate the root cause through tags: is it a memory retrieval error or a reasoning logic flaw? For example, in medical scenarios, the source of errors in drug recommendations can be traced.

NeuroSplit is suitable for scenarios requiring high interpretability and low hallucination rates, such as knowledge Q&A, educational tutoring, and enterprise knowledge bases. In the future, more tags can be added (e.g., {uncertainty} to identify uncertain content, {source} to trace knowledge sources), and the separation mechanism can be applied to vision-language models.

Although NeuroSplit is not large in scale, it has significant inspirational value: improving AI reliability does not necessarily require larger models or more data; clear structural design and ability separation can bring qualitative leaps, making it an important exploration direction for AI interpretability and safety research.