Divide, Harmonize, Then Conquer It: Shooting Multi-Commodity Flow Problems with Multimodal Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Yuan, Xinyu, Qiao, Yan, Wang, Zonghui, Chen, Wenzhi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the (Generative) Linear Sketching Problem
by: Yuan, Xinyu, et al.
Published: (2026)
by: Yuan, Xinyu, et al.
Published: (2026)
Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth
by: Yuan, Xinyu, et al.
Published: (2024)
by: Yuan, Xinyu, et al.
Published: (2024)
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
by: Zhang, Xiaoqing, et al.
Published: (2025)
by: Zhang, Xiaoqing, et al.
Published: (2025)
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024)
by: Zhang, Yizhou, et al.
Published: (2024)
LMTE: Putting the "Reasoning" into WAN Traffic Engineering with Language Models
by: Yuan, Xinyu, et al.
Published: (2026)
by: Yuan, Xinyu, et al.
Published: (2026)
Diffusion Generative Modelling for Divide-and-Conquer MCMC
by: Trojan, C., et al.
Published: (2024)
by: Trojan, C., et al.
Published: (2024)
Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
by: Zhou, Yan, et al.
Published: (2026)
by: Zhou, Yan, et al.
Published: (2026)
Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
by: Zhang, Weiliang, et al.
Published: (2025)
by: Zhang, Weiliang, et al.
Published: (2025)
Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach
by: Liu, Sicheng, et al.
Published: (2026)
by: Liu, Sicheng, et al.
Published: (2026)
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
by: Röpke, Willem, et al.
Published: (2024)
by: Röpke, Willem, et al.
Published: (2024)
Divide, Conquer, Combine Bayesian Decision Tree Sampling
by: Cochrane, Jodie A., et al.
Published: (2024)
by: Cochrane, Jodie A., et al.
Published: (2024)
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
by: Janati, Yazid, et al.
Published: (2024)
by: Janati, Yazid, et al.
Published: (2024)
Accurate and Scalable Matrix Mechanisms via Divide and Conquer
by: He, Guanlin, et al.
Published: (2026)
by: He, Guanlin, et al.
Published: (2026)
A Divide-and-Conquer Approach for Modeling Arrival Times in Business Process Simulation
by: Kirchdorfer, Lukas, et al.
Published: (2025)
by: Kirchdorfer, Lukas, et al.
Published: (2025)
Dynamic Dual Buffer with Divide-and-Conquer Strategy for Online Continual Learning
by: Dai, Congren, et al.
Published: (2025)
by: Dai, Congren, et al.
Published: (2025)
Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning
by: Hernández-Gutiérrez, Sergio, et al.
Published: (2025)
by: Hernández-Gutiérrez, Sergio, et al.
Published: (2025)
Divide-and-Conquer CoT: RL for Reducing Latency via Parallel Reasoning
by: Mahankali, Arvind, et al.
Published: (2026)
by: Mahankali, Arvind, et al.
Published: (2026)
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
by: Liu, Jiading, et al.
Published: (2022)
by: Liu, Jiading, et al.
Published: (2022)
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
by: Wang, Shang, et al.
Published: (2024)
by: Wang, Shang, et al.
Published: (2024)
Divide and Conquer Self-Supervised Learning for High-Content Imaging
by: Farndale, Lucas, et al.
Published: (2025)
by: Farndale, Lucas, et al.
Published: (2025)
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
by: Dai, Tianjie, et al.
Published: (2023)
by: Dai, Tianjie, et al.
Published: (2023)
Causal Discovery for Cross-Sectional Data Based on Super-Structure and Divide-and-Conquer
by: Wang, Wenyu, et al.
Published: (2026)
by: Wang, Wenyu, et al.
Published: (2026)
PLM-eXplain: Divide and Conquer the Protein Embedding Space
by: van Eck, Jan, et al.
Published: (2025)
by: van Eck, Jan, et al.
Published: (2025)
Divide-or-Conquer? Which Part Should You Distill Your LLM?
by: Wu, Zhuofeng, et al.
Published: (2024)
by: Wu, Zhuofeng, et al.
Published: (2024)
When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
by: Xu, Zhen, et al.
Published: (2025)
by: Xu, Zhen, et al.
Published: (2025)
Prompt, Divide, and Conquer: Bypassing Large Language Model Safety Filters via Segmented and Distributed Prompt Processing
by: Wahréus, Johan, et al.
Published: (2025)
by: Wahréus, Johan, et al.
Published: (2025)
A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition
by: Song, Yue
Published: (2026)
by: Song, Yue
Published: (2026)
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by: Yuan, Xinyu, et al.
Published: (2024)
by: Yuan, Xinyu, et al.
Published: (2024)
Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning
by: Duan, Yue, et al.
Published: (2025)
by: Duan, Yue, et al.
Published: (2025)
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
by: Sennesh, Eli, et al.
Published: (2024)
by: Sennesh, Eli, et al.
Published: (2024)
Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
by: Huang, Chengsong, et al.
Published: (2024)
by: Huang, Chengsong, et al.
Published: (2024)
Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
by: Ke, Fucai, et al.
Published: (2024)
by: Ke, Fucai, et al.
Published: (2024)
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution
by: Kościałkowski, Jan, et al.
Published: (2025)
by: Kościałkowski, Jan, et al.
Published: (2025)
Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media
by: Jain, Mayank Kumar, et al.
Published: (2025)
by: Jain, Mayank Kumar, et al.
Published: (2025)
Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
by: Chen, Evan, et al.
Published: (2026)
by: Chen, Evan, et al.
Published: (2026)
Learning-based Multi-continuum Model for Multiscale Flow Problems
by: Wang, Fan, et al.
Published: (2024)
by: Wang, Fan, et al.
Published: (2024)
Divide-and-Conquer Inference for Large-Scale Visual Recognition with Multimodal Large Language Models
by: Ye, Zhipeng, et al.
Published: (2026)
by: Ye, Zhipeng, et al.
Published: (2026)
SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction
by: Reshef, Ortal, et al.
Published: (2026)
by: Reshef, Ortal, et al.
Published: (2026)
Similar Items
-
On the (Generative) Linear Sketching Problem
by: Yuan, Xinyu, et al.
Published: (2026) -
Learning-based Sketches for Frequency Estimation in Data Streams without Ground Truth
by: Yuan, Xinyu, et al.
Published: (2024) -
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games
by: Zhang, Xiaoqing, et al.
Published: (2025) -
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024) -
LMTE: Putting the "Reasoning" into WAN Traffic Engineering with Language Models
by: Yuan, Xinyu, et al.
Published: (2026)