NextMem: Towards Latent Factual Memory for LLM-based Agents

• URL: http://arxiv.org/abs/2603.15634v1
• Date: Thu, 26 Feb 2026 14:35:27 GMT
• Title: NextMem: Towards Latent Factual Memory for LLM-based Agents
• Abstract summary: NextMem is a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory.<n>Extensive experiments demonstrate that NextMem achieves superior performance.
• Score: 58.35585202907478
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches to constructing factual memory face several limitations. Textual methods impose heavy context and indexing burdens, while parametric methods suffer from catastrophic forgetting and high costs. To address these challenges, we introduce NextMem, a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory while ensuring accurate reconstruction. For better optimization, we propose a two-stage training process, including autoregressive reconstruction alignment and progressive latent substitution. We also incorporate quantization to reduce storage overhead. Extensive experiments demonstrate that NextMem achieves superior performance, and excels in retrieval, robustness, and extensibility properties. We release our code and model checkpoints at https://github.com/nuster1128/NextMem.

Related papers

• Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
  Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
  arXiv  Detail & Related papers  (2026-07-15T08:32:40Z)
• AdMem: Advanced Memory for Task-solving Agents [13.611633561917513]
  Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.<n>We introduce a unified and automatic memory framework that integrates semantic, episodic, and procedural memory in a bi-level design combining short-term and long-term stores.
  arXiv  Detail & Related papers  (2026-06-05T00:11:57Z)
• How LoRA Remembers? A Parametric Memory Law for LLM Finetuning [29.141706047011862]
  We introduce the Parametric Memory Law, a robust power law linking loss reduction Delta L to effective parameters and sequence length.<n>At the token level, fine-grained analysis reveals a deterministic phase transition, demonstrating that a prediction probability of p > 0.5 constitutes a sufficient condition for recall.<n>MemFT is a threshold-guided optimization strategy that dynamically redistributes the training budget toward sub-threshold tokens.
  arXiv  Detail & Related papers  (2026-05-28T17:22:24Z)
• Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory [50.857546269660276]
  We introduce SeqMem-Eval, a diagnostic evaluation framework for sequentially evolving large language models (LLMs) memory.<n>Rather than focusing only on final performance, SeqMem-Eval evaluates how memory states evolve, generalize, consolidate experience, and retain useful information during sequential inference.
  arXiv  Detail & Related papers  (2026-05-14T20:15:22Z)
• MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution [52.29303869050117]
  Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction.<n>MemMA is a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths.
  arXiv  Detail & Related papers  (2026-03-19T10:15:59Z)
• MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [78.46301394559903]
  Large Language Models (LLMs) are increasingly used for long-duration tasks.<n>Current methods face a trade-off between cost and accuracy.<n>MemSifter is a novel framework that offloads the memory retrieval process to a small-scale proxy model.
  arXiv  Detail & Related papers  (2026-03-03T02:57:38Z)
• MemFly: On-the-Fly Memory Optimization via Information Bottleneck [35.420309099411874]
  Long-term memory enables large language model agents to tackle complex tasks through historical interactions.<n>Existing frameworks encounter a dilemma between compressing redundant information efficiently and maintaining precise retrieval for downstream tasks.<n>MemFly is a framework grounded in information bottleneck principles that facilitates on-the-fly memory evolution for LLMs.<n>MemFly substantially outperforms state-of-the-art baselines in memory coherence, response fidelity, and accuracy.
  arXiv  Detail & Related papers  (2026-02-08T09:37:25Z)
• Mem-T: Densifying Rewards for Long-Horizon Memory Agents [23.19373149519922]
  We introduce Mem-T, an autonomous memory agent that interfaces with a lightweight hierarchical memory database to perform dynamic updates and multi-turn retrieval over streaming inputs.<n>We also propose MoT-GRPO, a tree-guided reinforcement learning framework that transforms sparse terminal feedback into dense, step-wise supervision via memory operation tree backpropagation and hindsight credit assignment.
  arXiv  Detail & Related papers  (2026-01-30T14:23:33Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory [21.4675019810992]
  Concept-level memory is reusable, modular abstractions distilled from solution traces and stored in natural language.<n>We evaluate on ARC-AGI, a benchmark that stresses compositional generalization and abstract reasoning.<n>We find abstract concepts to be the most consistent memory design, outscoring the baseline at all tested inference compute scales.
  arXiv  Detail & Related papers  (2025-09-04T17:54:19Z)
• Memp: Exploring Agent Procedural Memory [72.41472703974935]
  Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or entangled in static parameters.<n>We propose Memp that distills past agent trajectories into both fine-grained, step-by-step instructions and higher-level, script-like abstractions.<n>We show that as the memory repository is refined, agents achieve steadily higher success rates and greater efficiency on analogous tasks.
  arXiv  Detail & Related papers  (2025-08-08T16:20:56Z)
• Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation [29.139579820699495]
  This work strives to reduce memory overhead in fine-tuning from perspectives of activation function and layer normalization. We apply our Approx-BP theory to backpropagation training and derive memory-efficient alternatives of GELU and SiLU activation functions. In addition, we introduce a Memory-Sharing Backpropagation strategy, which enables the activation memory to be shared by two adjacent layers.
  arXiv  Detail & Related papers  (2024-06-24T03:09:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.