Cover Image

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	So, Chi Chiu, Sun, Yueyue, Wang, Jun-Min, Yung, Siu Pang, Loh, Anthony Wai Keung, Chau, Chun Pong
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
by: So, Chi Chiu, et al.
Published: (2024)

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
by: DeepSeek-AI, et al.
Published: (2025)

A Comparison of DeepSeek and Other LLMs
by: Gao, Tianchen, et al.
Published: (2025)

RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability
by: Zhang, Yichi, et al.
Published: (2025)

Medical Reasoning in LLMs: An In-Depth Analysis of DeepSeek R1
by: Moell, Birger, et al.
Published: (2025)

Are DeepSeek R1 And Other Reasoning Models More Faithful?
by: Chua, James, et al.
Published: (2025)

Assessing the Capabilities of Novel Open‐Source Artificial Intelligence—DeepSeek in Helicobacter pylori‐Related Queries
by: Ren‐Chun Du, et al.
Published: (2025)

Benchmark-Driven Selection of AI: Evidence from DeepSeek-R1
by: Spelda, Petr, et al.
Published: (2025)

DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
by: Marjanović, Sara Vera, et al.
Published: (2025)

From Reasoning to Answer: Empirical, Attention-Based and Mechanistic Insights into Distilled DeepSeek R1 Models
by: Zhang, Jue, et al.
Published: (2025)

Learning to Reason: Training LLMs with GPT-OSS or DeepSeek R1 Reasoning Traces
by: Shmidman, Shaltiel, et al.
Published: (2025)

Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis
by: Zhao, Kaikai, et al.
Published: (2025)

DeepSeek-OCR: Contexts Optical Compression
by: Wei, Haoran, et al.
Published: (2025)

DeepSeek-V3 Technical Report
by: DeepSeek-AI, et al.
Published: (2024)

LLM & HPC:Benchmarking DeepSeek's Performance in High-Performance Computing Tasks
by: Nader, Noujoud, et al.
Published: (2025)

Can LLMs Assist Computer Education? an Empirical Case Study of DeepSeek
by: Xiao, Dongfu, et al.
Published: (2025)

Brief analysis of DeepSeek R1 and its implications for Generative AI
by: Mercer, Sarah, et al.
Published: (2025)

Output Length Effect on DeepSeek-R1's Safety in Forced Thinking
by: Li, Xuying, et al.
Published: (2025)

DeepSeek reshaping healthcare in China's tertiary hospitals
by: Chen, Jishizhan, et al.
Published: (2025)

Memory Analysis on the Training Course of DeepSeek Models
by: Zhang, Ping, et al.
Published: (2025)

Safety Evaluation of DeepSeek Models in Chinese Contexts
by: Zhang, Wenjing, et al.
Published: (2025)

DeepSeek-OCR 2: Visual Causal Flow
by: Wei, Haoran, et al.
Published: (2026)

Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures
by: Zhao, Chenggang, et al.
Published: (2025)

R1dacted: Investigating Local Censorship in DeepSeek's R1 Language Model
by: Naseh, Ali, et al.
Published: (2025)

Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models
by: Sharma, Shubham, et al.
Published: (2025)

o3-mini vs DeepSeek-R1: Which One is Safer?
by: Arrieta, Aitor, et al.
Published: (2025)

Mixture of Tunable Experts -- Behavior Modification of DeepSeek-R1 at Inference Time
by: Dahlke, Robert, et al.
Published: (2025)

Large Language Models for Transforming Healthcare: A Perspective on DeepSeek‐R1
by: Jinsong Zhou, et al.
Published: (2025)

A Review of DeepSeek Models' Key Innovative Techniques
by: Wang, Chengen, et al.
Published: (2025)

Safety Evaluation and Enhancement of DeepSeek Models in Chinese Contexts
by: Zhang, Wenjing, et al.
Published: (2025)

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment
by: Dong, Ben, et al.
Published: (2025)

An evaluation of DeepSeek Models in Biomedical Natural Language Processing
by: Zhan, Zaifu, et al.
Published: (2025)

Research on Collaborative Governance of AIGC Applications in the DeepSeek Era
by: Shengli Deng, et al.
Published: (2025)

DeepSeek Powered Solid Dosage Formulation Design and Development
by: Lin, Leqi, et al.
Published: (2025)

Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
by: Zhao, Enbo, et al.
Published: (2025)

Visual Merit or Linguistic Crutch? A Close Look at DeepSeek-OCR
by: Liang, Yunhao, et al.
Published: (2026)

DeepSeek in Healthcare: A Survey of Capabilities, Risks, and Clinical Applications of Open-Source Large Language Models
by: Ye, Jiancheng, et al.
Published: (2025)

Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
by: Huang, Donghao, et al.
Published: (2025)

Evaluating Test-Time Scaling LLMs for Legal Reasoning: OpenAI o1, DeepSeek-R1, and Beyond
by: Hu, Yinghao, et al.
Published: (2025)

DeepSeek-R1 vs. o3-mini: How Well can Reasoning LLMs Evaluate MT and Summarization?
by: Larionov, Daniil, et al.
Published: (2025)