Active Learning: Fueling Breakthroughs Across AI, From Fusion Energy to Medical Diagnosis

Latest 50 papers on active learning: Sep. 14, 2025

Active learning (AL) is transforming how AI models acquire knowledge, especially in data-scarce and cost-intensive domains. By intelligently selecting the most informative data points for labeling, AL minimizes human annotation effort while maximizing model performance. Recent research highlights a surge in innovative AL strategies, pushing the boundaries of what’s possible across a diverse range of applications—from optimizing complex engineering systems and enhancing cybersecurity to accelerating drug discovery and revolutionizing medical diagnostics.

The Big Idea(s) & Core Innovations

The central theme uniting these advancements is the quest for smarter data acquisition and robust model generalization with minimal human intervention. A significant number of papers tackle the challenge of reducing annotation costs, especially when dealing with Large Language Models (LLMs). For instance, ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval by Zihan Chen et al. from Beihang University and Westlake University, introduces a three-stage AL framework (diversity, similarity, uncertainty sampling) that allows LLMs to act as annotators for entity recognition in specialized domains. Their key insight: selectively annotating only 5%-10% of the dataset can achieve performance comparable to full dataset annotation, drastically cutting costs. Similarly, ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection leverages LLMs to automate critical labeling tasks for network anomaly detection, demonstrating how LLMs can generalize across diverse systems and failure modes, thereby reducing manual annotation needs.

Another critical innovation lies in making AL strategies more adaptive and context-aware. In reliability engineering, the “curse of dimensionality” is addressed by A Kriging-HDMR-based surrogate model with sample pool-free active learning strategy for reliability analysis by Wenxiong Li et al., which uses a sample pool-free approach and optimization to select informative samples, focusing accuracy where it matters most: near the limit state function. This theme is further explored in Balancing the exploration-exploitation trade-off in active learning for surrogate model-based reliability analysis via multi-objective optimization by Jonathan A. Morana and Pablo G. Morato, which proposes a multi-objective optimization framework to explicitly balance exploration and exploitation, offering greater flexibility than classical scalar-based strategies.

For complex scientific simulations, AL is proving indispensable. TGLF-SINN: Deep Learning Surrogate Model for Accelerating Turbulent Transport Modeling in Fusion by Yadi Cao et al. from UC San Diego and General Atomics, integrates Bayesian Active Learning (BAL) with physics-informed neural networks to reduce data requirements by up to 75% in fusion energy research. This resonates with LoUQAL: Low-fidelity informed Uncertainty Quantification for Active Learning in the chemical configuration space, where Vivin Vinod and Peter Zaspel use low-fidelity quantum chemical calculations to guide AL, significantly improving prediction accuracy and iteration efficiency.

AL is also tackling fundamental challenges in AI systems. Why Pool When You Can Flow? Active Learning with GFlowNets by Renfei Zhang et al. from Simon Fraser University introduces BALD-GFlowNet, a groundbreaking generative AL framework that replaces traditional pool-based acquisition with generative sampling guided by BALD rewards. This decouples acquisition cost from unlabeled dataset size, proving highly efficient for large-scale molecular discovery. In object detection, VILOD: A Visual Interactive Labeling Tool for Object Detection showcases how human guidance, supported by visual analytics and active learning, leads to better model performance and annotation quality, bridging human expertise with AI efficiency.

Under the Hood: Models, Datasets, & Benchmarks

The innovations discussed are often underpinned by specialized models, tailored datasets, and robust benchmarking strategies:

• ALLabel Framework: Utilizes Large Language Models (LLMs) as annotators. Validated on specialized domain datasets (e.g., materials science, chemistry).
• EZR: A modular tool for multi-objective optimization that integrates active sampling, learning, and explanation. Code available.
• TGLF-SINN: A deep learning surrogate model using physics-informed neural networks with Bayesian Active Learning, specifically for turbulent transport modeling in fusion devices.
• Kriging-HDMR: Combines Kriging models with High-Dimensional Model Representation and a sample pool-free active learning strategy for high-dimensional reliability analysis.
• Jump Gaussian Process (GP) Surrogates: Used in Active Learning of Piecewise Gaussian Process Surrogates by Chiwoo Park et al. to model non-stationary systems, with a MATLAB package available for the approach.
• DQS: A dissimilarity-based query strategy for unsupervised time series anomaly detection, leveraging dynamic time warping. Code available, tested on the PATH dataset.
• BALD-GFlowNet: Employs Generative Flow Networks to directly sample informative molecules for drug discovery, demonstrating efficiency on synthetic grid tasks and virtual screening for the JAK2 protein.
• Deep Active Learning for Lung Disease Classification: Integrates Bayesian Neural Networks (BNN) with weighted loss functions. Evaluated on Chest X-ray datasets, with code potentially available.
• EAAMARL: An Ensemble Active Adversarial Multi-Agent Reinforcement Learning framework for APT detection, using diverse RL agents (Q-learning, PPO, DQN). Code available.
• DRMD: A Deep Reinforcement Learning approach for malware detection under concept drift. Code available.
• LeMat-Traj Dataset & LeMaterial-Fetcher: A large-scale dataset of 120 million crystalline materials trajectories and an open-source library for its curation, valuable for training machine learning interatomic potentials. Dataset and Code available.

Impact & The Road Ahead

These research efforts highlight a transformative period for active learning. The ability to achieve high model performance with significantly less labeled data has profound implications across industries. In healthcare, from accelerated lung disease diagnosis (Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance) to human-centric medical dialogue systems (Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning), AL is making AI diagnostics more efficient and accessible. In cybersecurity, novel frameworks like SAGE (SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks) and DRMD (DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift) are building more robust and adaptive threat detection systems against evolving adversarial attacks. For engineering and physical sciences, surrogate models guided by AL are accelerating complex simulations in fusion energy and reliability analysis, potentially cutting years off R&D cycles.

The trend towards integrating active learning with large language models and reinforcement learning is particularly exciting. This synergy is paving the way for more autonomous and intelligent agents that can learn from minimal interaction, adapt to dynamic environments, and even generate their own informative data. Future research will likely focus on further refining these hybrid approaches, exploring new uncertainty quantification techniques, and developing more sophisticated human-in-the-loop systems that seamlessly blend AI efficiency with human intuition. The journey towards data-efficient, robust, and interpretable AI is well underway, with active learning at its forefront.