When LLMs Know but Fail to Reason: Injecting Memory for Reasoning Enhancement

Large language models (LLMs) have demonstrated strong capabilities on complex reasoning tasks. However, recent studies~\citep{gekhman2026thinking,song2026large,jin2025disentangling,cheng2024understanding} suggest that when handling knowledge-intensive reasoning tasks, LLMs often fail to effectively utilize the knowledge acquired during pre-training, which limits their reasoning performance. To investigate how internal knowledge (also termed memory) is used by an LLM when dealing with a reasoning query, we propose the Memory-to-Reasoning Alignment (MRA) metric to measure the memory engagement level of an LLM on the reasoning query. We empirically verify that failures in reasoning may correspond to insufficient memory engagement. Inspired by this, we propose a training-free Memory-Guided Activation Injection (MGAI) method to increase the memory engagement level of an LLM, in order to improve the reasoning capability of an LLM. Specifically, given a query, MGAI generates an intervention vector and applies it to the reasoning representation to inject the corresponding memory information into the reasoning representation of the LLM. Furthermore, we propose an adaptive intervention strategy to control which queries require intervention and the magnitude of the intervention. Experiments show that our method consistently improves performance across multiple reasoning benchmarks while largely preserving the memory and general capabilities of LLMs.