Latest Long-Tail Learning Research Papers

The newest Long-Tail Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Long-Tail Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
  Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026

  Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…

• Turing–Entropic Tail Classification: A Nonparametric Approach to Tail Inference
  Jialin Zhang, Zhiyi Zhang · Methodology And Computing I... · Sep 1, 2026
• MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification
  Zhou Zelong, Zhang Tianming, Yang Zhengyi, Tang Yifu et al. · arXiv · Aug 25, 2026

  Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minority representations, while conventional node and neighborhood information provides insuffi…

• On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
  Qinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang et al. · arXiv · Aug 18, 2026

  Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods…

• Benchmarking Class Imbalance Mitigation Strategies Across Deep CNN Architectures for Skin Cancer Classification
  Irshad Ahmad, Muhammad Khubaib, Saleh M. Altowaijri · Diagnostics · Aug 14, 2026

  Background/Objectives: Class imbalance is one of the major challenges in automated skin lesion classification since the number of categories of malignant and clinically significant skin lesions is normally less than the benign ones. However…

• Open Long-Tailed Multimodal 3D Model Classification Based on Sample-Enhanced Category-Space Learning
  Yuansa Wang, Xueyao Gao, Chunxiang Zhang, Yongzeng Xue · Journal of Imaging · Aug 10, 2026

  With the rapid development of three-dimensional (3D) sensing technologies, multimodal 3D model classification has achieved significant progress. However, most existing methods are developed under closed and balanced assumptions, which limit…

• Optimization-driven feature learning for long-tailed object detection in optical remote sensing imagery
  Nishi Madaan, Rahul Malik, Utsav Upadhyay, Alok Kumar et al. · Scientific Reports · Aug 3, 2026
• Network analysis highlights socio-demographic patterns of stone tool-using primates
  Gwennan T. L. Giraud, Theo D. R. O’Malley, Jonathan S. Reeves, Amanda Tan et al. · Scientific Reports · Aug 1, 2026

  Abstract While the nutritional benefits and social learning of tool use in primates have been widely studied, far less is known about how tool use is associated with social life. We investigated the social aspects of tool use in a hybrid po…

• Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation
  Priya Tomar, Aditya Parikh, Christian Bauckhage, Rafet Sifa · arXiv · Jul 31, 2026

  Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance, which results from relatively lower proportions of small and limitedly exposed structu…

• Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark
  Manpreet Singh, Akshatha Srikantha, Shyamal Lakhanpal · arXiv · Jul 29, 2026

  High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under severe class imbalance and asymmetric error costs. Standard marginal conformal prediction (…

• TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
  Zhouchonghao Wu, Akshay Rangesh, Weixin Li, Wei-Jer Chang et al. · arXiv · Jul 14, 2026

  Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long ta…

• Robust lightweight facial expression recognition on edge devices: mitigating long-tailed distributions and real-world noises
  Yuchen Song, Yining Yuan, Yufei Guo, Xuerong Zhao et al. · OpenAlex · Jul 13, 2026

  Deploying lightweight facial expression recognition (FER) on resource-constrained edge devices presents challenges beyond mere model compression. This work addresses two critical issues that arise in this context: the exacerbated performanc…

• Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
  Cláudio Lúcio do Val Lopes, Lucca Machado da Silva · arXiv · Jul 10, 2026

  Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class and failing to balance anomaly interdiction with customer fric…

• Long-Tail Learning for Three-Dimensional Pavement Distress Segmentation Using Point Clouds Reconstructed from a Consumer Camera
  Pengjian Cheng, Junyan Yi, Zhongshi Pei, Zengxin Liu et al. · Remote Sensing · Mar 27, 2026

  The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring an…

• Knowledge graph-enhanced long-tail learning approach for traditional Chinese medicine syndrome differentiation
  Weikang Kong, Chuanbiao Wen, Luo Yue · Digital Chinese Medicine · Mar 1, 2026
• EdgeTail: Mitigating Long-Tail Visual Problems in Continual Learning at Edge
  Yuzhong Ouyang, Xiaoning Wu, Menglin Yang, Rui Han et al. · ACM Trans. Internet Things · Dec 25, 2025

  Large vision and language models deployed at edge encounter continuously evolving input distributions, including not only new tasks but also highly unbalanced long-tail classes. For example, smart-surveillance cameras frequently capture com…

• Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
  Nvidia Yan Wang, Wen-Jie Luo, Junjie Bai, Yulong Cao et al. · arXiv.org · Oct 30, 2025

  End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tail scenarios where supervision is sparse and causal understan…

• Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning
  Canran Xiao, Chuangxin Zhao, Zong Ke, Fei Shen · arXiv.org · Oct 20, 2025

  Long-tail imbalance is endemic to multi-label learning: a few head labels dominate the gradient signal, while the many rare labels that matter in practice are silently ignored. We tackle this problem by casting the task as a cooperative pot…

• APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
  Yuzhen Zhou, Jiajun Li, Yusheng Su, Gowtham Ramesh et al. · arXiv.org · Sep 23, 2025

  Reinforcement learning (RL) has become a cornerstone in advancing large-scale pre-trained language models (LLMs). Successive generations, including GPT-o series, DeepSeek-R1, Kimi-K1.5, Grok 4, and GLM-4.5, have relied on large-scale RL tra…

• Interpretable Preference Elicitation: Aligning User Intent with Controllable Long-tailed Learning
  HaiBin Wen, Shuangwang, Xucong Wang, YuHeng Yang et al. · ICLR 2026 Conference Withdrawn Submission · Sep 20, 2025

  Long-tailed recognition remains a significant challenge, where models often struggle with tail class performance and adaptability to diverse user preferences. While recent controllable paradigms leveraging hypernetworks allow numerical spec…

• Class-Adaptive Rectification with Experts for Robust Long-Tailed Noisy Label Learning
  Mengke Li, Haiquan Ling, Lihao Chen, Yang Lu et al. · Submitted to ICLR 2026 · Sep 19, 2025

  Real-world datasets frequently exhibit long-tailed class distributions alongside noisy labels, posing compounded challenges for robust learning. While recent methods have made progress, they often neglect the uneven impact of label noise ac…

• Efficient Long-Tail Learning in Latent Space by sampling Synthetic Data
  Nakul Sharma · arXiv.org · Sep 19, 2025

  Imbalanced classification datasets pose significant challenges in machine learning, often leading to biased models that perform poorly on underrepresented classes. With the rise of foundation models, recent research has focused on the full,…

• Generative Active Learning for Long-Tail Trajectory Prediction via Controllable Diffusion Model
  Daehee Park, Monu Surana, Pranav Desai, A. Mehta et al. · IEEE International Conference on Computer Vision · Jul 30, 2025

  While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works addressed this by modifying model architectures, such as using hype…

• AMD: Adaptive Momentum and Decoupled Contrastive Learning Framework for Robust Long-Tail Trajectory Prediction
  Bin Rao, Haicheng Liao, Yanchen Guan, Chengyue Wang et al. · IEEE International Conference on Computer Vision · Jul 2, 2025

  Accurately predicting the future trajectories of traffic agents is essential in autonomous driving. However, due to the inherent imbalance in trajectory distributions, tail data in natural datasets often represents more complex and hazardou…

• LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
  Huaiyuan Yao, Peng-Fei Li, Bu Jin, Yupeng Zheng et al. · arXiv.org · May 22, 2025

  Recent advances in autonomous driving research towards motion planners that are robust, safe, and adaptive. However, existing rule-based and data-driven planners lack adaptability to long-tail scenarios, while knowledge-driven methods offer…

• LIFT+: Lightweight Fine-Tuning for Long-Tail Learning
  Jiang-Xin Shi, Tong Wei, Yu-Feng Li · IEEE Transactions on Pattern Analysis and Machine Intelligence · Apr 17, 2025

  The fine-tuning paradigm has emerged as a prominent approach for addressing long-tail learning tasks in the era of foundation models. However, the impact of fine-tuning strategies on long-tail learning performance remains unexplored. In thi…

• Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving
  Xiaoshan Zhou, C. Menassa, V. Kamat · Expert systems with applications · Apr 14, 2025

  Robots with wheeled, quadrupedal, or humanoid forms are increasingly integrated into built environments. However, unlike human social learning, they lack a critical pathway for intrinsic cognitive development, namely, learning from human fe…

• Class and Attribute-Aware Logit Adjustment for Generalized Long-Tail Learning
  Xiaoling Zhou, Ou Wu, Nan Yang · AAAI Conference on Artificial Intelligence · Apr 11, 2025

  Compared to conventional long-tail learning, which focuses on addressing class-wise imbalances, generalized long-tail (GLT) learning considers that samples within each class still conform to long-tailed distributions due to varying attribut…

• Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond
  Qiongxiu Li, Xiaoyu Luo, Yiyi Chen, Johannes Bjerva · arXiv.org · Mar 10, 2025

  The role of memorization in machine learning (ML) has garnered significant attention, particularly as modern models are empirically observed to memorize fragments of training data. Previous theoretical analyses, such as Feldman's seminal wo…

• Dynamic Subclass-Balancing Contrastive Learning for Long-Tail Pedestrian Trajectory Prediction With Progressive Refinement
  Biao Yang, Kai Yan, Chuan Hu, Hongyu Hu et al. · IEEE Transactions on Automation Science and Engineering · Jan 1, 2025

  Pedestrian trajectory prediction is critical for understanding human behavior. The prevailing approaches employ neural networks to predict trajectories from large amounts of trajectory data. However, pedestrian trajectory data exhibits a lo…