Cover Image

Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Shiqi, Zhang, Jinghan, Zhu, Tongyao, Liu, Wei, Gao, Siyang, Xiong, Miao, Li, Manling, He, Junxian
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
by: Chen, Shiqi, et al.
Published: (2025)

Internalizing World Models via Self-Play Finetuning for Agentic RL
by: Chen, Shiqi, et al.
Published: (2025)

SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?
by: Chen, Shiqi, et al.
Published: (2026)

In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
by: Chen, Shiqi, et al.
Published: (2024)

Does Learning Mathematical Problem-Solving Generalize to Broader Reasoning?
by: Zhou, Ruochen, et al.
Published: (2025)

Beyond SFT: Reinforcement Learning for Safer Large Reasoning Models with Better Reasoning Ability
by: Jia, Jinghan, et al.
Published: (2025)

Unconstrained Model Merging for Enhanced LLM Reasoning
by: Zhang, Yiming, et al.
Published: (2024)

Reasoning Pattern Alignment Merging for Adaptive Reasoning
by: Zhong, Zhaofeng, et al.
Published: (2026)

Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
by: Zhao, Bingchen, et al.
Published: (2024)

ChemVLR: Prioritizing Reasoning in Perception for Chemical Vision-Language Understanding
by: Zhao, Xuanle, et al.
Published: (2026)

On the Universal Truthfulness Hyperplane Inside LLMs
by: Liu, Junteng, et al.
Published: (2024)

Non-myopic Generation of Language Models for Reasoning and Planning
by: Ma, Chang, et al.
Published: (2024)

On the Perception Bottleneck of VLMs for Chart Understanding
by: Liu, Junteng, et al.
Published: (2025)

Belief Revision: The Adaptability of Large Language Models Reasoning
by: Wilie, Bryan, et al.
Published: (2024)

Transferring Textual Preferences to Vision-Language Understanding through Model Merging
by: Li, Chen-An, et al.
Published: (2025)

Diving into Self-Evolving Training for Multimodal Reasoning
by: Liu, Wei, et al.
Published: (2024)

ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging
by: Yang, Junyao, et al.
Published: (2026)

A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
by: Qu, Xiaoye, et al.
Published: (2025)

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
by: Liu, Junteng, et al.
Published: (2025)

Mitigating Overthinking through Reasoning Shaping
by: Song, Feifan, et al.
Published: (2025)

From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
by: Wang, Haonan, et al.
Published: (2025)

The Thinking Spectrum: An Empirical Study of Tunable Reasoning in LLMs through Model Merging
by: Lan, Xiaochong, et al.
Published: (2025)

From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
by: Huang, Yuzhen, et al.
Published: (2025)

Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
by: Wu, Han, et al.
Published: (2025)

Superficial Self-Improved Reasoners Benefit from Model Merging
by: Yuan, Xiangchi, et al.
Published: (2025)

MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark for ESG Tasks
by: Zhang, Lei, et al.
Published: (2025)

CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling
by: Fan, Chongyu, et al.
Published: (2025)

Compression Represents Intelligence Linearly
by: Huang, Yuzhen, et al.
Published: (2024)

HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
by: Wei, Yanbin, et al.
Published: (2026)

Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
by: Li, Yanda, et al.
Published: (2024)

Entropy-based Exploration Conduction for Multi-step Reasoning
by: Zhang, Jinghan, et al.
Published: (2025)

Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
by: Zhu, Yingjie, et al.
Published: (2024)

ReasonGRM: Enhancing Generative Reward Models through Large Reasoning Models
by: Chen, Bin, et al.
Published: (2025)

Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception
by: Huang, Yuncheng, et al.
Published: (2023)

On Asymmetric Optimization of Reasoning and Perception in Vision-Language Model Post-Training
by: Wu, Xueqing, et al.
Published: (2026)

MM-THEBench: Do Reasoning MLLMs Think Reasonably?
by: Huang, Zhidian, et al.
Published: (2026)

Utilizing Large Language Models for Information Extraction from Real Estate Transactions
by: Zhao, Yu, et al.
Published: (2024)

ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code
by: Xie, Jian, et al.
Published: (2025)

Learn to Reason Efficiently with Adaptive Length-based Reward Shaping
by: Liu, Wei, et al.
Published: (2025)

Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models
by: Pan, Zhenyu, et al.
Published: (2024)