CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory

• URL: http://arxiv.org/abs/2601.02708v2
• Date: Sat, 10 Jan 2026 14:50:48 GMT
• Title: CREAM: Continual Retrieval on Dynamic Streaming Corpora with Adaptive Soft Memory
• Authors: HuiJeong Son, Hyeongu Kang, Sunho Kim, Subeen Ho, SeongKu Kang, Dongha Lee, Susik Yoon,
• Abstract summary: CREAM is a self-supervised framework for memory-based continual retrieval.<n>It adapts to both seen and unseen topics in an unsupervised setting.<n> Experiments on two benchmark datasets demonstrate that CREAM exhibits superior adaptability and retrieval accuracy.
• Score: 19.64051996386645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information retrieval (IR) in dynamic data streams is a crucial task, as shifts in data distribution degrade the performance of AI-powered IR systems. To mitigate this issue, memory-based continual learning has been widely adopted for IR. However, existing methods rely on a fixed set of queries with ground-truth documents, which limits generalization to unseen data, making them impractical for real-world applications. To enable more effective learning with unseen topics of a new corpus without ground-truth labels, we propose CREAM, a self-supervised framework for memory-based continual retrieval. CREAM captures the evolving semantics of streaming queries and documents into dynamically structured soft memory and leverages it to adapt to both seen and unseen topics in an unsupervised setting. We realize this through three key techniques: fine-grained similarity estimation, regularized cluster prototyping, and stratified coreset sampling. Experiments on two benchmark datasets demonstrate that CREAM exhibits superior adaptability and retrieval accuracy, outperforming the strongest method in a label-free setting by 27.79% in Success@5 and 44.5% in Recall@10 on average, and achieving performance comparable to or even exceeding that of supervised methods.

Related papers

• SimpleMem: Efficient Lifelong Memory for LLM Agents [73.74399447715052]
  We introduce SimpleMem, an efficient memory framework based on semantic lossless compression.<n>We propose a three-stage pipeline designed to maximize information density and token utilization.<n> Experiments on benchmark datasets show that our method consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost.
  arXiv  Detail & Related papers  (2026-01-05T21:02:49Z)
• CADIC: Continual Anomaly Detection Based on Incremental Coreset [0.0]
  We propose a novel CAD framework where all tasks share a unified memory bank.<n>During training, the method incrementally updates embeddings within a fixed-size coreset.<n>In the inference phase, anomaly scores are computed via a nearest-neighbor matching mechanism.
  arXiv  Detail & Related papers  (2025-11-10T03:13:11Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes [7.036974567001374]
  ReFine is a framework that guides generation toward domain-specific feature distribution.<n>Experiments on various regression and classification benchmarks demonstrate that ReFine consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-12T04:34:46Z)
• Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation [54.61034867177997]
  Caching inference responses allows them to be retrieved without another forward pass through the Large Language Models.<n>Traditional exact-match caching overlooks the semantic similarity between queries, leading to unnecessary recomputation.<n>We present a principled, learning-based framework for semantic cache eviction under unknown query and cost distributions.
  arXiv  Detail & Related papers  (2025-08-11T06:53:27Z)
• Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations [0.0]
  We propose a hierarchical clustering-based retrieval method that eliminates the need to predefine k.<n>Our approach maintains the accuracy and relevance of system responses while adaptively selecting semantically relevant content.<n>Our framework is simple to implement and easily integrates with existing RAG pipelines, making it a practical solution for real-world applications under limited resources.
  arXiv  Detail & Related papers  (2025-06-16T15:34:29Z)
• Adapting to Non-Stationary Environments: Multi-Armed Bandit Enhanced Retrieval-Augmented Generation on Knowledge Graphs [23.357843519762483]
  Recent studies have demonstrated that leveraging the Retrieval-Augmented Generation framework, combined with Knowledge Graphs, robustly enhances the reasoning capabilities of Large language models.<n>We introduce a Multi-objective Multi-Armed Bandit enhanced RAG framework, supported by multiple retrieval methods with diverse capabilities.<n>Our method significantly outperforms baseline methods in non-stationary settings while achieving state-of-the-art performance in stationary environments.
  arXiv  Detail & Related papers  (2024-12-10T15:56:03Z)
• Locally Adaptive One-Class Classifier Fusion with Dynamic $\ell$p-Norm Constraints for Robust Anomaly Detection [17.93058599783703]
  We introduce a framework that dynamically adjusts fusion weights based on local data characteristics. Our method incorporates an interior-point optimization technique that significantly improves computational efficiency. The framework's ability to adapt to local data patterns while maintaining computational efficiency makes it particularly valuable for real-time applications.
  arXiv  Detail & Related papers  (2024-11-10T09:57:13Z)
• Adaptive Retention & Correction: Test-Time Training for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task.<n>We name our approach Adaptive Retention & Correction (ARC)<n>ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Noisy Self-Training with Synthetic Queries for Dense Retrieval [49.49928764695172]
  We introduce a novel noisy self-training framework combined with synthetic queries. Experimental results show that our method improves consistently over existing methods. Our method is data efficient and outperforms competitive baselines.
  arXiv  Detail & Related papers  (2023-11-27T06:19:50Z)
• Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust? [91.682459306359]
  In continual learning (CL), an AI agent learns from non-stationary data streams under dynamic environments. In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge. The generalization and memorization performance of the proposed framework are theoretically analyzed.
  arXiv  Detail & Related papers  (2023-09-18T21:00:01Z)
• LORD: Leveraging Open-Set Recognition with Unknown Data [10.200937444995944]
  LORD is a framework to Leverage Open-set Recognition by exploiting unknown data. We identify three model-agnostic training strategies that exploit background data and applied them to well-established classifiers.
  arXiv  Detail & Related papers  (2023-08-24T06:12:41Z)

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.