Multi-Task Optimization

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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CRT-HMAR: Causal Requirement Tracing-Guided Hierarchical Multi-Agent Regulation for Open-Task-Aware Infrared-Visible Image Fusion
Oct 7, 2026Zengyi Yang, Shuai Yuan, Zhong-Cheng Wu +3Infrared-Visible Image Fusion Multi-Task Optimization
School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China · Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
A Broader Look at Model Merging: Rethinking Implicit Regularization Induced by Task Arithmetic
Oct 6, 2026Sin-Han Yang, Shih-Cheng Huang, Chieh-Yen Lin +3Task Vector Merging Multi-Task Optimization
Appier AI Research · National Taiwan University
In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization
Sep 22, 2026Tingyang Wei, Haofeng Wu, Jiao Liu +3Bayesian Optimization Multi-Task Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore · Institute of Advanced Intelligence and Computing, Agency for Science, Technology and Research, Singapore · Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research, Singapore +1
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning
Sep 14, 2026Zebin Chen, Fei Xing, Yang Chen +4Multi-Task Learning Multi-Task Optimization
Southern University of Science and Technology Shenzhen, China · City University of Hong Kong Hong Kong SAR, China · Shanxi University Taiyuan, China
Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer
Sep 12, 2026Tingyang Wei, Haofeng Wu, Ananda Phan Iman +3Multi-Task Optimization Multi-Objective Optimization
College of Computing and Data Science Nanyang Technological University Singapore · Department of AI Convergence Gwangju Inst. of Sci. & Tech. (GIST) South Korea · Centre for Frontier AI Research (CFAR) A*STAR Singapore
Decentralized Multitask Learning over Learned Task Graphs
Aug 27, 2026Zirui Wan, Stefan VlaskiGraph Structure Learning Multi-Task Learning
Department of Electrical and Electronic Engineering, Imperial College London, UK
MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning
Aug 12, 2026Shiji Zhou, Kunlin Lyu, Lei Zhang +2Neural Network Optimization Multi-Task Learning
Institute of Artificial Intelligence, Beihang University · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University · Beijing Academy of Artificial Intelligence (BAAI) +1
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
Aug 5, 2026Yue Han, Ziniu LiuSupervised Fine-Tuning Multi-Task Learning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Jul 18, 2026Yangjing Wang, Ouya Wang, Shenglong Zhou +1Wireless Foundation Models Hierarchical Representation Learning
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2BX London, U.K. · School of Mathematics and Statistics, Beijing Jiaotong University, China
Capacity and Redundancy Trade-offs in Multi-Task Learning
Jul 17, 2026Asif KhanMulti-Task Learning Multi-Task Optimization
1Harvard Medical School, Boston, USA
Pitfalls and Remedies for Multi-Task Bayesian Optimization
Jul 10, 2026Carl Hvarfner, Sam Daulton, Max Balandat +1Bayesian Optimization Hyperparameter Transfer
Meta
The Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
May 21, 2026Erjian Zhang, Yatong Hao, Liejun Wang +1Radiology Report Generation Gradient Interference
School of Computer Science and Technology, Xinjiang University, Urumqi, China · Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Urumqi, China
optimize_anything: A Universal API for Optimizing any Text Parameter
May 19, 2026Lakshya A Agrawal, Donghyun Lee, Shangyin Tan +11Black-Box Optimization Large Language Model-Guided Optimization
UC Berkeley USA · MIT USA
PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
May 13, 2026Anjir Ahmed Chowdhury, Syed Zawad, Xiaolong Ma +2LLM Fine-Tuning Multi-Task Learning
Department of Computer Science University of Houston · IBM Research · Argonne National Laboratory
TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
May 12, 2026Yuanpeng Li, Gefei Lin, Annie Qu +1Multi-Task RL Proximal Policy Optimization
UC Irvine · George Washington University · UC Santa Barbara +1
A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
May 9, 2026Zhiyao Zhang, Myeung Suk Oh, Zhen Qin +3Bilevel Optimization Multi-Task Optimization
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA · Meta Platforms, Inc., Menlo Park, CA, USA.
CoAction: Cross-task Correlation-aware Pareto Set Learning
May 3, 2026Xinyue Chen, Yingxuan Liang, Yiqin Huang +3Multi-Task Learning Multi-Task Optimization
Guangdong University of Technology, Guangzhou, China · Xiamen University, Xiamen, China
Near-optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
May 1, 2026Shihong Ding, Fangyu Du, Cong FangRepresentation Learning Multi-Task Learning
State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, China · School of Mathematical Sciences, Peking University, China · Institute for Artificial Intelligence, Peking University, China
Multi-Task Optimization over Networks of Tasks
Apr 23, 2026Julian Hatzky, Thomas Bartz-Beielstein, A. E. Eiben +1Evolutionary Optimization Quality-Diversity Optimization
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands · TH Köln, Cologne, Germany
Analysis of Multitasking Pareto Optimization for Monotone Submodular Problems
Apr 16, 2026Liam Wigney, Frank NeumannSubmodular Optimization Evolutionary Optimization
Optimisation and Logistics School of Computer Science and Information Technology Adelaide University Adelaide, Australia
Asymptotic Behavior of Multi--Task Learning: Implicit Regularization and Double Descent Effects
Mar 5, 2026Ayed M. Alrashdi, Oussama Dhifallah, Houssem SifaouDouble Descent Neural Network Generalization
Department of Electrical Engineering, College of Engineering, University of Ha’il, P.O. Box 2440, Ha’il, 81441, Saudi Arabia · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA · Department of Electrical and Electronic Engineering, King’s College London – Strand, London WC2R 2LS – London, UK
Graph Coloring for Multi-Task Learning
Sep 21, 2025Santosh Patapati, Ian NoronhaGradient Interference Multi-Task Learning
Stony Brook University AI Innovation Institute, Stony Brook, NY 11794, USA · Purdue University, West Lafayette, IN 47907, USA
Learning Task Mixtures from Task Affinities: A Probabilistic Graphical Model for Supervised Fine-Tuning
Jul 16, 2025Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee +3Supervised Fine-Tuning Multi-Task Learning
IIT Bombay · IBM Research · Red Hat AI Innovation +1
( $θ_l, θ_u$ )-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
Mar 11, 2025Tingyang Wei, Jiao Liu, Abhishek Gupta +2Evolutionary Optimization Multi-Task Optimization
Variational Model Merging for Pareto Front Estimation in Multitask Finetuning
Dec 11, 2024Hugo Monzón Maldonado, Nico Daheim, Thomas Möllenhoff +2Bayesian Inference Multi-Task Learning
RIKEN Center for AI Project, Japan · Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany · National Research Center for Applied Cybersecurity ATHENE, Germany
DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models
Date pendingQuanhao Li, Junqiu Yu, Kaixun Jiang +7Diffusion Model Distillation Multi-Task RL
Fudan University, China · Alibaba Group, China

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

CRT-HMAR: Causal Requirement Tracing-Guided Hierarchical Multi-Agent Regulation for Open-Task-Aware Infrared-Visible Image Fusion

Zengyi Yang, Shuai Yuan, Zhong-Cheng Wu, Juan Cheng+2

School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China · Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Infrared-Visible Image Fusion Multi-Task Optimization

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Infrared and visible (IR-VIS) image fusion integrates complementary multimodal information into a single fused image to support downstream vision tasks. However, existing methods are typically tailored to seen tasks within a fixed task set and struggle to generalize to unseen tasks, which restricts their applicability in real-world open-task scenarios. To address this issue, this paper proposes CRT-HMAR, a Causal Requirement Tracing-Guided Hierarchical Multi-Agent Regulation Framework for open-task-aware IR-VIS image fusion. CRT-HMAR introduces a Causal Requirement Tracing Task Localization mechanism, which actively intervenes in key image information and observes task-network response variations to map task-specific semantic preferences into image-level causal requirement maps. Based on these maps, a requirement analysis agent aggregates task-specific requirement knowledge to adaptively guide requirement-customized image fusion. Moreover, CRT-HMAR incorporates History-Analysis Multi-Objective Balancing and Task-Level-Correction Conflict Mitigation mechanisms, jointly constructing a hierarchical regulation chain of "requirement interpretation - task balancing - conflict mitigation". Through multiple collaborative agents, CRT-HMAR dynamically regulates key processes including open-task requirement modeling, multi-task balanced optimization, and gradient conflict mitigation. Extensive experiments on open-task scenarios involving five downstream tasks demonstrate that CRT-HMAR significantly improves generalization to unseen tasks while maintaining the performance and balance of seen tasks. Overall, CRT-HMAR shifts IR-VIS image fusion from task-oriented modeling toward requirement-oriented modeling, promoting its extension from closed-task settings to real-world open-task scenarios.

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