Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation

• URL: http://arxiv.org/abs/2603.05873v1
• Date: Fri, 06 Mar 2026 03:49:23 GMT
• Title: Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation
• Abstract summary: We propose a memory-augmented segmentation agent (MemSeg-Agent) that shifts adaptation from weight space to memory space.<n>MemSeg-Agent conditions a fixed backbone with lightweight static, few-shot, and test-time working memories.<n>Experiments on four public datasets demonstrate strong performance and robustness to domain shift.
• Score: 18.026494321724943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is fundamental to clinical workflows, yet models trained on a single dataset often fail to generalize across institutions, scanners, or patient populations. While vision foundation models have shown great promise in addressing this challenge, their deployment typically requires task-specific fine-tuning, which introduces substantial communication overhead in federated learning and prevents continuous knowledge evolution during deployment. In this work, we propose a memory-augmented segmentation agent (MemSeg-Agent) that shifts adaptation from weight space to memory space, enabling few-shot learning, federated supervised learning, and test-time adaptation within a unified architecture. MemSeg-Agent conditions a fixed backbone with lightweight static, few-shot, and test-time working memories, which are dynamically composed by an agentic controller. In federated settings, we update compact memory units instead of model parameters, substantially reducing communication overhead. Experiments on four public datasets demonstrate strong performance and robustness to domain shift: Static memory alone matches or surpasses strong supervised baselines with high parameter efficiency, and test-time working memory further improves in-domain and cross-domain performance without fine-tuning. Overall, MemSeg-Agent introduces a new paradigm for scalable and adaptive medical image segmentation in the era of agentic AI.

Related papers

• UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams [77.52140119830241]
  We propose UniMem, a self-routing framework for autonomous memory management.<n>UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways.<n>Experiments on long-horizon streaming task sequences show that UniMem consistently outperforms baselines while maintaining execution fidelity.
  arXiv  Detail & Related papers  (2026-07-28T17:28:21Z)
• AdaMEM: Test-Time Adaptive Memory for Language Agents [27.178795834085296]
  We propose the Adaptive Memory Agent (AdaMEM), a novel framework for agent test-time adaptation.<n>Without updating model parameters online, AdaMEM adapts agent behavior via a hybrid memory architecture.<n>AdaMEM significantly outperforms static memory baselines, achieving relative gains of up to 13% on ALFWorld and 11% on WebShop.
  arXiv  Detail & Related papers  (2026-06-04T04:06:08Z)
• UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory [46.87954895079213]
  Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)<n>Existing methods predominately optimize memory management while treating memory extraction as a static process.<n>We propose Unified Memory Extraction and Management (UMEM) to jointly optimize a Large Language Model to simultaneous extract and manage memories.
  arXiv  Detail & Related papers  (2026-02-11T08:58:41Z)
• Memory-Integrated Reconfigurable Adapters: A Unified Framework for Settings with Multiple Tasks [29.90641701972515]
  Organisms constantly pivot between tasks such as evading predators, foraging, traversing rugged terrain, and socializing, often within milliseconds.<n>We propose a unified framework that integrates Hopfield-style associative memory modules atop a shared backbone.<n> Empirical evaluations on standard benchmarks confirm that our AM-augmented architecture significantly enhances adaptability and retention.
  arXiv  Detail & Related papers  (2025-11-30T15:45:55Z)
• Enhancing Semantic Segmentation with Continual Self-Supervised Pre-training [11.897717409259492]
  Self-supervised learning (SSL) has emerged as a central paradigm for training foundation models.<n>We propose GLARE, a novel continual self-supervised pre-training task designed to enhance downstream segmentation performance.
  arXiv  Detail & Related papers  (2025-09-22T14:11:02Z)
• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents [84.62985963113245]
  We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks.<n>At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning.<n>We show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task.
  arXiv  Detail & Related papers  (2025-06-18T19:44:46Z)
• STAMP: Scalable Task And Model-agnostic Collaborative Perception [24.890993164334766]
  STAMP is a task- and model-agnostic, collaborative perception pipeline for heterogeneous agents.<n>It minimizes computational overhead, enhances scalability, and preserves model security.<n>As a first-of-its-kind framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy.
  arXiv  Detail & Related papers  (2025-01-24T16:27:28Z)
• Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation [10.746776960260297]
  We propose a new continual whole-body organ segmentation model with light-weighted low-rank adaptation (LoRA) We first train and freeze a pyramid vision transformer (PVT) base segmentation model on the initial task, then continually add light-weighted trainable LoRA parameters to the frozen model for each new learning task. Our proposed model continually segments new organs without catastrophic forgetting and meanwhile maintaining a low parameter increasing rate.
  arXiv  Detail & Related papers  (2024-10-07T02:00:13Z)
• Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation [49.5901368256326]
  We propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) in segmenting medical images. Our DAPSAM achieves state-of-the-art performance on two medical image segmentation tasks with different modalities.
  arXiv  Detail & Related papers  (2024-09-19T07:28:33Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• Memory-Guided Semantic Learning Network for Temporal Sentence Grounding [55.31041933103645]
  We propose a memory-augmented network that learns and memorizes the rarely appeared content in TSG tasks. MGSL-Net consists of three main parts: a cross-modal inter-action module, a memory augmentation module, and a heterogeneous attention module.
  arXiv  Detail & Related papers  (2022-01-03T02:32:06Z)
• Hierarchical Variational Memory for Few-shot Learning Across Domains [120.87679627651153]
  We introduce a hierarchical prototype model, where each level of the prototype fetches corresponding information from the hierarchical memory. The model is endowed with the ability to flexibly rely on features at different semantic levels if the domain shift circumstances so demand. We conduct thorough ablation studies to demonstrate the effectiveness of each component in our model.
  arXiv  Detail & Related papers  (2021-12-15T15:01:29Z)

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.