Multi-Task Learning’s New Frontiers: From Synergistic Models to Life-Saving AI

Latest 50 papers on multi-task learning: Oct. 20, 2025

Multi-task learning (MTL) is revolutionizing AI by enabling models to learn multiple objectives simultaneously, often leading to improved efficiency, generalization, and robustness. This approach mimics how humans learn, leveraging shared knowledge across related tasks to perform better on each. Recent research highlights a significant push towards more intelligent, adaptive, and specialized MTL frameworks, tackling challenges from medical diagnosis to industrial automation and complex language understanding.

The Big Idea(s) & Core Innovations

The core challenge in MTL often lies in managing task interference and fostering positive knowledge transfer. Recent papers showcase groundbreaking solutions. For instance, SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation by Jung, Lee, Han, and Hong from Sungkyunkwan University and NAVER AI Lab, redefines model merging. Instead of just mitigating interference, SyMerge aims for task synergy, where tasks actively enhance each other. Their key insight is that adapting even a single task-specific layer can significantly improve cross-task compatibility, stabilized by a robust expert-guided self-labeling strategy. This lightweight framework achieves state-of-the-art results across vision, dense prediction, and NLP tasks. Complementing this, Weight Weaving: Parameter Pooling for Data-Free Model Merging by Chaves, Valle, and Avila (Recod.ai Lab., UNICAMP) offers a data-free model merging technique. By pooling parameters across scaling factors, Weight Weaving (https://arxiv.org/pdf/2510.13921) bypasses the need for privileged data, consistently improving existing methods in vision tasks with gains up to 15.9 percentage points.

In the realm of language models, Dynamic Prompt Fusion for Multi-Task and Cross-Domain Adaptation in LLMs proposes a novel dynamic prompt scheduling mechanism. Researchers from Hofstra University and Carnegie Mellon University demonstrate that dynamically adjusting prompts based on contextual importance significantly improves cross-domain generalization, outperforming existing baselines on SuperGLUE and MMLU benchmarks. Meanwhile, Cheng et al. from Jilin University introduce MeTA-LoRA: Data-Efficient Multi-Task Fine-Tuning for Large Language Models (https://arxiv.org/pdf/2510.11598), a two-stage framework that dramatically improves data efficiency in multi-task fine-tuning for LLMs, enabling rapid adaptation with minimal task-specific data and robust cross-task knowledge transfer. Another significant leap is seen in Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning by Yang et al. from Hokkaido University (https://arxiv.org/pdf/2510.00570), which reduces gradient conflicts and enhances expert specialization through a novel Adaptive Shared Experts (ASE) design within a LoRA-based Mixture-of-Experts (MoE) framework.

Addressing critical challenges in optimization, Wang et al. from Yale and University of Pennsylvania, in Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective (https://arxiv.org/pdf/2502.00604), introduce a gradient alignment score and demonstrate how second-order optimizers like SOAP implicitly mitigate directional gradient conflicts in Physics-informed Neural Networks (PINNs), achieving state-of-the-art results on challenging PDE benchmarks, including turbulent flows at high Reynolds numbers. This ties into the AW-EL-PINNs framework by Li and Zeng (Southwest University, China) (https://arxiv.org/pdf/2509.25262), which uses adaptive loss weighting to dynamically balance components in optimal control problems for Euler-Lagrange systems, showcasing superior accuracy over baseline PINN methods.

Under the Hood: Models, Datasets, & Benchmarks

This wave of innovation is powered by novel architectures, specialized datasets, and rigorous benchmarks:

• SyMerge: A minimalist single-layer adaptation approach for model merging, demonstrating state-of-the-art performance across vision, dense prediction (segmentation, depth, surface normals), and NLP benchmarks. Code available at https://aim-skku.github.io/SyMerge/.
• Weight Weaving: An efficient weight pooling method that improves existing data-free model merging techniques, with code available at https://github.com/VirtualSpaceman/weight_weaving.
• MeTA-LoRA: A two-stage framework for data-efficient multi-task fine-tuning of LLMs, maintaining strong performance with significantly less task-specific data. Paper URL doubles as code reference: https://arxiv.org/pdf/2510.11598.
• Adaptive Shared Experts with LoRA-Based Mixture of Experts: Utilizes a LoRA-MoE design for efficient MTL, preserving expressivity while reducing computational overhead. Paper URL as code reference: https://arxiv.org/pdf/2510.00570.
• ETR-fr Dataset: Introduced by Ledoyen et al. (Université Caen Normandie) in Facilitating Cognitive Accessibility with LLMs (https://arxiv.org/pdf/2510.00662), this is the first high-quality dataset compliant with European ETR guidelines for easy-to-read text generation. Code at https://github.com/FrLdy/ETR-PEFT-Composition.
• M3ST-DTI: A multi-task learning model for drug-target interactions, leveraging multi-modal features and multi-stage alignment. Code at https://github.com/M3ST-DTI.
• SwasthLLM: A unified framework for cross-lingual, multi-task, and meta-learning zero-shot medical diagnosis using contrastive representations. Code: https://github.com/SwasthLLM-team/swasthllm and dataset: https://www.kaggle.com/datasets/pranav092005/multilingual-dataset.
• AortaDiff: A unified multitask diffusion framework for contrast-free AAA imaging, available at https://arxiv.org/pdf/2510.01498, with code at https://github.com/yuxuanou623/AortaDiff.git.
• World Model for AI Autonomous Navigation in Mechanical Thrombectomy: Employs TD-MPC2 for multi-task endovascular navigation, with implementation code at https://github.com/nicklashansen/tdmpc2.
• UniFlow-Audio: A unified non-autoregressive framework for audio generation from omni-modalities. Code and models at https://wsntxxn.github.io/uniflow_audio.
• PHG-MAE: Unifies neural graphs and masked autoencoders for semi-supervised multi-modal multi-task learning. Dronescapes dataset extension available at https://sites.google.com/view/dronescapes-dataset.
• MEJO: Surgical Triplet Recognition framework that uses MLLMs and Coordinated Gradient Learning, demonstrating state-of-the-art on CholecT45 and CholecT50 datasets (https://arxiv.org/pdf/2509.12893).
• MAESTRO: A multi-task 3D perception framework evaluated on nuScenes and Occ3D benchmarks. Code URL is assumed to be https://github.com/MAESTRO-Project/maestro.
• EvHand-FPV: An efficient event-based 3D hand tracking framework for first-person view, also introducing a new dataset. Code available at https://github.com/zen5x5/EvHand-FPV.

Impact & The Road Ahead

The impact of these advancements is far-reaching. In medicine, multi-task learning is driving safer and more accurate diagnostics, from A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation (University of Florida) using structured clinical features and vision-language models (https://arxiv.org/pdf/2506.23584) to Multi-Task Learning with Feature-Similarity Laplacian Graphs for Predicting Alzheimer’s Disease Progression (UCSF) (https://arxiv.org/pdf/2510.10433), which significantly improves prediction accuracy for Alzheimer’s progression. The move towards Contrast-Free AAA Imaging with AortaDiff (University of Oxford) promises enhanced patient safety by reducing the need for contrast agents.

Drug discovery is seeing accelerated innovation with Multitask finetuning and acceleration of chemical pretrained models by Adrian et al. (Merck & Co., NVIDIA) (https://arxiv.org/pdf/2510.12719) and M3ST-DTI (USTC), both leveraging multi-task learning to predict drug properties and interactions more accurately. Industrial applications are benefiting from improved quality control with Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects by Dong et al. (Harbin Institute of Technology), which introduces novel metrics to better align with real-world quality control goals. Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series (https://arxiv.org/pdf/2510.09679) by Xu et al. (University of Calgary) offers more accurate land cover change detection in remote sensing, essential for environmental monitoring.

Personalized recommendation systems are becoming more sophisticated with frameworks like DRGrad: A Personalized Information Surgery for Multi-Task Learning Recommendations (https://arxiv.org/pdf/2510.09643) from Whisper Bond Technologies Inc. and Tongji University, and GRADE: Personalized Multi-Task Fusion via Group-relative Reinforcement Learning by Hong et al. (Kuaishou Technology), which dynamically adapt to user preferences and reduce task conflicts. These developments underscore a future where AI systems are not only more capable but also more interpretable and adaptable to complex, real-world scenarios. The path ahead involves further exploring dynamic task adaptation, robust uncertainty handling, and efficient knowledge transfer, pushing the boundaries of what multi-task learning can achieve across every domain imaginable.