Bottom line: The “Knowing-Using Gap” phenomenon describes how LLMs memorize new facts, but their internal representations are not forwarded to the layers responsible for reasoning.
Researchers have investigated why large language models can memorize new facts but cannot use them for reasoning tasks. They identified the problem as misalignment between the storage and utilization of knowledge representations within the model.
When fine-tuning large language models with new knowledge content, a systematic problem emerges: models quickly memorize new facts but cannot apply them to downstream reasoning tasks. Researchers formalize this phenomenon as the “Knowing-Using Gap”—characterized by an accuracy gap and temporal delay between memorization and generalization.
To understand this mechanism, the authors developed a technique called “Self-Patching,” which maps the spatial propagation dynamics of new knowledge inside the model. Self-Patching identifies activation sites where repositioning representations substantially improves failed generalization cases. The results support a “Knowledge-Circuit Misalignment” hypothesis: memorized representations exist internally but are not forwarded to computationally efficient layers—a routing problem rather than a storage problem.
Based on this diagnosis, the researchers developed a simple heuristic strategy that recovers 58 to 75 percent of the theoretically achievable improvement in failed generalization cases. Experiments were conducted across domains to ensure the robustness of the findings.
For CTOs, this means that fine-tuning strategies should not only be optimized for memorization but must explicitly ensure that new knowledge representations reach the layers actually responsible for reasoning and problem-solving.
Source: arxiv.org · Published July 9, 2026
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