DRAMA: Domain Retrieval using Adaptive Module Allocation

• URL: http://arxiv.org/abs/2602.14960v1
• Date: Mon, 16 Feb 2026 17:38:24 GMT
• Title: DRAMA: Domain Retrieval using Adaptive Module Allocation
• Authors: Pranav Kasela, Marco Braga, Ophir Frieder, Nazli Goharian, Gabriella Pasi, Raffaele Perego,
• Abstract summary: DRAMA (Domain Retrieval using Adaptive Module Allocation) is an energy- and parameter-efficient framework designed to reduce the environmental footprint of neural retrieval.<n>This paper introduces DRAMA, an energy- and parameter-efficient framework designed to reduce the environmental footprint of neural retrieval.
• Score: 19.15437181769345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural models are increasingly used in Web-scale Information Retrieval (IR). However, relying on these models introduces substantial computational and energy requirements, leading to increasing attention toward their environmental cost and the sustainability of large-scale deployments. While neural IR models deliver high retrieval effectiveness, their scalability is constrained in multi-domain scenarios, where training and maintaining domain-specific models is inefficient and achieving robust cross-domain generalisation within a unified model remains difficult. This paper introduces DRAMA (Domain Retrieval using Adaptive Module Allocation), an energy- and parameter-efficient framework designed to reduce the environmental footprint of neural retrieval. DRAMA integrates domain-specific adapter modules with a dynamic gating mechanism that selects the most relevant domain knowledge for each query. New domains can be added efficiently through lightweight adapter training, avoiding full model retraining. We evaluate DRAMA on multiple Web retrieval benchmarks covering different domains. Our extensive evaluation shows that DRAMA achieves comparable effectiveness to domain-specific models while using only a fraction of their parameters and computational resources. These findings show that energy-aware model design can significantly improve scalability and sustainability in neural IR.

Related papers

• MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Models [51.506429027626005]
  Memory for Time Series (MEMTS) is a lightweight and plug-and-play method for retrieval-free domain adaptation in time series forecasting.<n>Key component of MEMTS is a Knowledge Persistence Module (KPM), which internalizes domain-specific temporal dynamics.<n>This paradigm shift enables MEMTS to achieve accurate domain adaptation with constant-time inference and near-zero latency.
  arXiv  Detail & Related papers  (2026-02-14T14:00:06Z)
• World Model Implanting for Test-time Adaptation of Embodied Agents [29.514831254621438]
  In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining.<n>We present a world model implanting framework (WorMI) that combines the reasoning capabilities of large language models with independently learned, domain-specific world models.<n>We evaluate our WorMI on the VirtualHome and ALFWorld benchmarks, demonstrating superior zero-shot and few-shot performance compared to several LLM-based approaches.
  arXiv  Detail & Related papers  (2025-09-04T07:32:16Z)
• Retrieval-augmented reasoning with lean language models [5.615564811138556]
  We develop a retrieval augmented conversational agent capable of interpreting complex, domain-specific queries.<n>Our system integrates a dense retriever with fine-tuned Qwen2.5-Instruct models.<n>All implementation details and code are publicly released to support and adaptation across domains.
  arXiv  Detail & Related papers  (2025-08-15T10:38:15Z)
• Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need [25.45880299215022]
  Domain Incremental Learning (DIL) offers a solution by enabling continual model adaptation.<n>Existing PIDIL methods struggle with parameter selection accuracy as the number of domains and corresponding classes grows.<n>We propose SOYO, a lightweight framework that improves domain selection in PIDIL.
  arXiv  Detail & Related papers  (2025-05-29T17:58:57Z)
• Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization [68.41367635546183]
  Single Domain Generalization aims to train models with consistent performance across diverse scenarios using data from a single source.<n>We propose Discriminative Domain Reassembly and Soft-Fusion (DRSF), a training framework leveraging synthetic data to improve model generalization.
  arXiv  Detail & Related papers  (2025-03-17T18:08:03Z)
• MoDEM: Mixture of Domain Expert Models [23.846823652305027]
  We propose a novel approach to enhancing the performance and efficiency of large language models (LLMs) We introduce a system that utilizes a BERT-based router to direct incoming prompts to the most appropriate domain expert model. Our research demonstrates that this approach can significantly outperform general-purpose models of comparable size.
  arXiv  Detail & Related papers  (2024-10-09T23:52:54Z)
• Variational Model Perturbation for Source-Free Domain Adaptation [64.98560348412518]
  We introduce perturbations into the model parameters by variational Bayesian inference in a probabilistic framework. We demonstrate the theoretical connection to learning Bayesian neural networks, which proves the generalizability of the perturbed model to target domains.
  arXiv  Detail & Related papers  (2022-10-19T08:41:19Z)
• Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval [54.349418995689284]
  We propose a novel Dense Retrieval (DR) framework named Disentangled Dense Retrieval ( DDR) to support effective domain adaptation for DR models. By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data. DDR significantly improves ranking performance compared to strong DR baselines and substantially outperforms traditional retrieval methods in most scenarios.
  arXiv  Detail & Related papers  (2022-08-11T11:18:50Z)
• IterMiUnet: A lightweight architecture for automatic blood vessel segmentation [10.538564380139483]
  This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model. It overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
  arXiv  Detail & Related papers  (2022-08-02T14:33:14Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)

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.