PAPER: INTRODUCTIONThe ability to process and reason over long-context information is a critical frontier for modern Large Language Models (LLMs). While Transformer-based architectures (Vaswani et al., 2017) have demonstrated remarkable capabilities, their self-attention mechanism incurs a computational and memory cost that scales quadratically with sequence length (Tay et al., 2022). This limitation presents a significant barrier to modeling extensive documents, lengthy conversations, or entire codebases. In response, a new class of architectures with near-linear complexity has emerged, most notably State Space Models (SSMs) like the Mamba architecture (Gu & Dao, 2023), which have become a promising alternative for efficient long-sequence modeling.However, the efficiency of SSMs comes at a cost. Their core mechanism, which relies on an exponential decay kernel to compress past information into a fixed-size state, inevitably leads to rapid information forgetting and challenges in length extrapolation (Yen et al., 2024;Chen et al., 2024). This makes it difficult for the model to accurately recall specific details from distant parts of a sequence, a phenomenon often termed the "lost in the middle" problem (Liu et al., 2023a). To mitigate this, one line of research has focused on enhancing the model's internal memory. This approach is inspired by advances in efficient Transformers that employ chunking or sparse attention mechanisms to preserve long-term context, such as Longformer (Beltagy et al., 2020), BigBird (Zaheer et al., 2020), and Native Sparse Attention (NSA) (Yuan et al., 2025). Following this direction, we first extend Mamba with a chunk-wise internal memory, which substantially improves its ability to retrieve information from within the input sequence.Under review as a conference paper at ICLR 2026 While strengthening internal recall is a necessary step, a more fundamental challenge is integrating vast external world knowledge without compromising efficiency. The conventional paradigm for this is Retrieval-Augmented Generation (RAG) (Lewis et al., 2020), which appends retrieved documents to the model's input. Although effective, this method is computationally expensive, as it directly inflates the sequence length (Li et al., 2025), and often results in a superficial concatenation rather than a deep fusion of knowledge (Asai et al., 2024). Pioneering work has explored pre-training models to learn to retrieve, such as REALM (Guu et al., 2020) and RETRO (Borgeaud et al., 2022), but these still rely on processing raw text during training and inference. The challenge of integrating pre-encoded external knowledge-such as knowledge graph embeddings or document vectorsdirectly into a model's core architecture during pre-training has remained largely unaddressed.To address these limitations, we propose the Plastic Unified Memory Network (PUM-Net). We argue that a truly effective long-context model must not only maintain a robust internal memory but also seamlessly fuse it with a vast, static external knowledge base. PUM-Net is designed to achieve this through a novel joint pre-training methodology. Our contributions are threefold:1. For Internal Memory: We introduce a chunk-wise long-term memory mechanism for Mamba-based SSMs. This architectural enhancement significantly mitigates the inherent forgetting problem for information within the input context and improves in-sequence recall over long distances. 2. For External Memory: We propose a novel pre-training methodology to deeply integrate a static, pre-encoded external knowledge base into the model's learning process. This is the first demonstration of how SSMs can be trained to fuse external knowledge without the costly concatenation of raw text. 3. A Unified Architecture and System: We combine these advancements in our Plastic Unified Memory Network (PUM-Net). We demonstrate that the resulting synergy from the deep integration of both internal and external memory systems leads to substantial improvements on challenging long-context benchmarks.
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REVIEW
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**Summary Of The Paper**

The paper introduces the **Plastic Unified Memory Network (PUM-Net)**, a novel architecture that enhances the long-context modeling capabilities of State Space Models (SSMs) by integrating a **dynamic internal memory** and a **static external memory**. The internal memory is implemented as a **chunk-wise mechanism** that partitions input sequences into manageable segments, improving the model's ability to retain and recall information over long ranges. The external memory leverages **pre-encoded knowledge** (e.g., Wikipedia embeddings) and is integrated into the model during pre-training using a **joint training methodology**, avoiding the need for raw-text concatenation. The model employs a **staged parallel computation** training regime and a **bi-directional associative interaction module (AIM)** to fuse internal and external memory representations. Evaluation on long-context benchmarks (PG-19, ProofPile, CodeParrot) shows that PUM-Net outperforms Mamba and attention-based variants in terms of perplexity, in-context recall, and question-answering performance, while also exhibiting superior computational efficiency.

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**Strengths**

- **Clear Motivation**: The paper addresses a critical issue in SSMs — the inability to retain long-range dependencies due to the exponential decay kernel — and offers a principled solution through the chunk-wise internal memory mechanism.
- **Comprehensive Methodology Description**: The architecture is meticulously described, with equations (e.g., Eq. 1 for external memory key-value generation, Eq. 3 for internal memory chunking) providing a solid technical foundation.
- **Detailed Training Procedure**: The **staged parallel computation** training approach is clearly outlined, with Algorithm 1 offering a precise roadmap for reproduction. This ensures transparency and facilitates future replication.
- **Empirical Validation**: The results on long-context benchmarks (Tables 1, 2; Figures 2–5) are compelling, with PUM-Net showing marked improvements over Mamba and attention-based variants, especially on knowledge-intensive tasks like CodeParrot.
- **Efficiency Analysis**: The paper reports a **3.9x speedup over NSA** and **26.5x over Flash-Attention** at 64k tokens, supported by quantitative evaluations, which highlights the practical appeal of the model.

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**Weaknesses**

- **Limited Comparison with RAG-Based Approaches**: Despite the emphasis on external memory integration, the paper does not directly compare PUM-Net with established RAG methods like REALM or RETRO. Section H acknowledges this but fails to justify why such a comparison is omitted, leaving the novelty of the external memory integration questionable.
- **Insufficient Hyperparameter Sensitivity Analysis**: Critical hyperparameters such as **chunk size**, **number of top-k memories**, and **fusion mechanism** are mentioned but not analyzed systematically (e.g., in ablation studies). This limits the reader’s ability to understand the optimal configuration of the model.
- **No Statistical Significance Testing**: The reported performance gains lack confidence intervals or p-values, making it uncertain whether the improvements are statistically significant or merely due to variance in the experimental runs.
- **Minimal Discussion of Edge Cases**: There is little exploration of **failure modes** or **edge cases** (e.g., extremely long sequences, ambiguous retrieval situations), which is essential for assessing the robustness of the model.
- **Ambiguous Role of External Memory in Specific Tasks**: While the overall benefit of external memory is noted, the paper does not investigate whether it contributes differently to **fact-based** versus **inference-based** tasks, nor does it explore the extent to which it influences **specific subtasks**.

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**Questions For The Authors**

1. **Why is there no direct comparison with RAG-style methods like REALM or RETRO**, given that the external memory integration in PUM-Net appears conceptually aligned with these approaches?
2. **What is the rationale behind using the same encoder $ E_{\text{key}} $ for both internal and external memory keys**? Could this lead to **interference or misalignment** between the two memory systems?
3. **How was the pre-trained Mamba-2.7B model selected** for generating the external memory states (Eq. 1)? Are there any **guarantees that its latent states align semantically with the PUM-Net model**?
4. **Could the use of different projection matrices in the AIM module (e.g., $ W_{Q1}, W_{K1}, W_{V1} $) lead to asymmetric information flow** between internal and external memory modules?
5. **Is the claimed efficiency advantage of PUM-Net validated on other long-context models like BigBird or Longformer**, or is the current comparison limited to attention-based baselines that may not be suitable for true long-context modeling?

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**Limitations Not Addressed By The Authors**

- **Scalability Beyond Extremely Long Sequences**: The paper evaluates up to 64k tokens, but it does not discuss how the model behaves at **sequence lengths beyond this limit**, which is important for real-world applications involving ultra-long texts.
- **Lack of Multi-Modal Extension**: The paper is entirely focused on textual inputs. There is **no mention of extending PUM-Net to handle images, audio, or video**, which limits its applicability to broader modalities.
- **No Publicly Available Implementation or Hyperparameters**: The absence of a public codebase or detailed hyperparameter settings hinders independent verification and replication of the results.
- **No Exploration of Adaptive External Memory Mechanisms**: The external memory is static, yet the paper notes that **adaptive external memory** is a potential future direction. There is no analysis of how such adaptation might be implemented or evaluated.
- **Ethical Considerations for External Knowledge Sources**: While the Ethics Statement acknowledges potential biases in the external knowledge base, there is **no systematic evaluation of bias or factual correctness** in the retrieved information.

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**Soundness:** 3 (Good)

While the paper presents a coherent and technically sound architecture, certain aspects (like the lack of statistical rigor and comparison with RAG methods) weaken the overall soundness. The methodology is well-described, and the results are convincing, but the absence of deeper analysis reduces confidence in the conclusions.

**Contribution:** 3 (Good)

The contribution lies in the integration of a dual-memory system into SSMs, which is novel in scope. However, the lack of direct comparison with prior work (especially RAG) and insufficient ablation studies weakens the perceived novelty.

**Confidence:** 3 (Moderate)

The paper is reasonably confident in its claims, but the lack of detailed experimentation, statistical analysis, and comparative benchmarks reduces certainty regarding the robustness and generalizability of the findings.

**Rating:** 7 (Accept)

The paper presents a valuable contribution to the field of long-context modeling and introduces a novel architecture with strong empirical support. However, the lack of comprehensive analysis and comparisons prevents it from being a standout submission. With revisions addressing the above concerns, it deserves acceptance.

**Brief Justification For Rating:**
The paper introduces a novel and well-motivated architecture with strong empirical results. However, the lack of statistical analysis, limited comparison with related methods, and minimal exploration of edge cases hinder its impact. With appropriate revisions, it is acceptable for publication.

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