{"ID":2846702,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01581","arxiv_id":"2511.01581","title":"ExplicitLM: Decoupling Knowledge from Parameters via Explicit Memory Banks","abstract":"Large language models suffer from knowledge staleness and lack of interpretability due to implicit knowledge storage across entangled network parameters, preventing targeted updates and reasoning transparency. We propose ExplicitLM, a novel architecture featuring a million-scale external memory bank storing human-readable knowledge as token sequences, enabling direct inspection and modification. We design a differentiable two-stage retrieval mechanism with efficient coarse-grained filtering via product key decomposition (reducing complexity from $\\mathcal{O}(N \\cdot |I|)$ to $\\mathcal{O}(\\sqrt{N} \\cdot |I|)$) and fine-grained Gumbel-Softmax matching for end-to-end training. Inspired by dual-system cognitive theory, we partition knowledge into frozen explicit facts (20%) and learnable implicit patterns (80%), maintained through Exponential Moving Average updates for stability. ExplicitLM achieves up to 43.67% improvement on knowledge-intensive tasks versus standard Transformers, with 3.62$\\times$ gains in low-data regimes (10k samples). Analysis shows strong correlations between memory retrieval and performance, with correct predictions achieving 49% higher hit rates. Unlike RAG systems with frozen retrieval, our jointly optimized architecture demonstrates that interpretable, updatable models can maintain competitive performance while providing unprecedented knowledge transparency.","short_abstract":"Large language models suffer from knowledge staleness and lack of interpretability due to implicit knowledge storage across entangled network parameters, preventing targeted updates and reasoning transparency. We propose ExplicitLM, a novel architecture featuring a million-scale external memory bank storing human-reada...","url_abs":"https://arxiv.org/abs/2511.01581","url_pdf":"https://arxiv.org/pdf/2511.01581v1","authors":"[\"Chengzhang Yu\",\"Zening Lu\",\"Chenyang Zheng\",\"Chiyue Wang\",\"Yiming Zhang\",\"Zhanpeng Jin\"]","published":"2025-11-03T13:53:19Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}