Improving LLMs' Generalized Reasoning Abilities by Graph Problems
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Qifan, Chen, Nuo, Li, Zehua, Peng, Miao, Tang, Jing, Li, Jia |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems
by: Zhang, Qifan, et al.
Published: (2026)
by: Zhang, Qifan, et al.
Published: (2026)
Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners
by: Peng, Miao, et al.
Published: (2025)
by: Peng, Miao, et al.
Published: (2025)
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
by: Tang, Jianheng, et al.
Published: (2024)
by: Tang, Jianheng, et al.
Published: (2024)
NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems
by: Wang, Yuyao, et al.
Published: (2025)
by: Wang, Yuyao, et al.
Published: (2025)
How does Misinformation Affect Large Language Model Behaviors and Preferences?
by: Peng, Miao, et al.
Published: (2025)
by: Peng, Miao, et al.
Published: (2025)
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring
by: Anand, Avinash, et al.
Published: (2024)
by: Anand, Avinash, et al.
Published: (2024)
Chain of Execution Supervision Promotes General Reasoning in Large Language Models
by: Chen, Nuo, et al.
Published: (2025)
by: Chen, Nuo, et al.
Published: (2025)
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
by: Chen, Nuo, et al.
Published: (2023)
by: Chen, Nuo, et al.
Published: (2023)
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping
by: Peng, Miao, et al.
Published: (2026)
by: Peng, Miao, et al.
Published: (2026)
Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration
by: Zhang, Qifan, et al.
Published: (2026)
by: Zhang, Qifan, et al.
Published: (2026)
Diversity of Thought Improves Reasoning Abilities of LLMs
by: Naik, Ranjita, et al.
Published: (2023)
by: Naik, Ranjita, et al.
Published: (2023)
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
by: Zhang, Qifan, et al.
Published: (2024)
by: Zhang, Qifan, et al.
Published: (2024)
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
by: Cheng, Kewei, et al.
Published: (2024)
by: Cheng, Kewei, et al.
Published: (2024)
Navigating the Labyrinth: Evaluating LLMs' Ability to Reason About Search Problems
by: Borazjanizadeh, Nasim, et al.
Published: (2024)
by: Borazjanizadeh, Nasim, et al.
Published: (2024)
MAR:Multi-Agent Reflexion Improves Reasoning Abilities in LLMs
by: Ozer, Onat, et al.
Published: (2025)
by: Ozer, Onat, et al.
Published: (2025)
Evaluating the Reasoning Abilities of LLMs on Underrepresented Mathematics Competition Problems
by: Golladay, Samuel, et al.
Published: (2025)
by: Golladay, Samuel, et al.
Published: (2025)
Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models
by: Yang, Xiao-Wen, et al.
Published: (2025)
by: Yang, Xiao-Wen, et al.
Published: (2025)
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
by: Chen, Weile, et al.
Published: (2026)
by: Chen, Weile, et al.
Published: (2026)
Improving Arithmetic Reasoning Ability of Large Language Models through Relation Tuples, Verification and Dynamic Feedback
by: Miao, Zhongtao, et al.
Published: (2024)
by: Miao, Zhongtao, et al.
Published: (2024)
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations
by: Huang, Kaixuan, et al.
Published: (2025)
by: Huang, Kaixuan, et al.
Published: (2025)
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
by: Liu, Yijun, et al.
Published: (2024)
by: Liu, Yijun, et al.
Published: (2024)
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
by: Chen, Nuo, et al.
Published: (2023)
by: Chen, Nuo, et al.
Published: (2023)
Seeing with You: Perception-Reasoning Coevolution for Multimodal Reasoning
by: Miao, Ziqi, et al.
Published: (2026)
by: Miao, Ziqi, et al.
Published: (2026)
RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs
by: Fernandez, Nigel, et al.
Published: (2025)
by: Fernandez, Nigel, et al.
Published: (2025)
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs
by: Fathullah, Yassir, et al.
Published: (2023)
by: Fathullah, Yassir, et al.
Published: (2023)
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
by: Chen, Yingjian, et al.
Published: (2026)
by: Chen, Yingjian, et al.
Published: (2026)
GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning
by: Ying, Yuchen, et al.
Published: (2026)
by: Ying, Yuchen, et al.
Published: (2026)
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
by: Dai, Xinnan, et al.
Published: (2024)
by: Dai, Xinnan, et al.
Published: (2024)
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
by: Cai, Wenrui, et al.
Published: (2025)
by: Cai, Wenrui, et al.
Published: (2025)
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
by: Liu, An, et al.
Published: (2024)
by: Liu, An, et al.
Published: (2024)
Unveiling the Compositional Ability Gap in Vision-Language Reasoning Model
by: Li, Tianle, et al.
Published: (2025)
by: Li, Tianle, et al.
Published: (2025)
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
by: Chen, Zui, et al.
Published: (2024)
by: Chen, Zui, et al.
Published: (2024)
AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms?
by: Sun, Henan, et al.
Published: (2026)
by: Sun, Henan, et al.
Published: (2026)
An Experimental Study of Competitive Market Behavior Through LLMs
by: Jia, Jingru, et al.
Published: (2024)
by: Jia, Jingru, et al.
Published: (2024)
Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs
by: Luo, Hang, et al.
Published: (2025)
by: Luo, Hang, et al.
Published: (2025)
ControlMath: Controllable Data Generation Promotes Math Generalist Models
by: Chen, Nuo, et al.
Published: (2024)
by: Chen, Nuo, et al.
Published: (2024)
Evaluating the Logical Reasoning Abilities of Large Reasoning Models
by: Liu, Hanmeng, et al.
Published: (2025)
by: Liu, Hanmeng, et al.
Published: (2025)
Reasoning Does Not Necessarily Improve Role-Playing Ability
by: Feng, Xiachong, et al.
Published: (2025)
by: Feng, Xiachong, et al.
Published: (2025)
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
by: Zhang, Junkai, et al.
Published: (2025)
by: Zhang, Junkai, et al.
Published: (2025)
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine
by: Jiang, Zhuoxuan, et al.
Published: (2025)
by: Jiang, Zhuoxuan, et al.
Published: (2025)
Similar Items
-
Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems
by: Zhang, Qifan, et al.
Published: (2026) -
Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners
by: Peng, Miao, et al.
Published: (2025) -
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
by: Tang, Jianheng, et al.
Published: (2024) -
NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems
by: Wang, Yuyao, et al.
Published: (2025) -
How does Misinformation Affect Large Language Model Behaviors and Preferences?
by: Peng, Miao, et al.
Published: (2025)