Are Large Language Models Capable of Deep Relational Reasoning? A Benchmark-Driven Analysis of DeepSeek-R1, DeepSeek-V3, and GPT-4o
1. Introduction
Large Language Models (LLMs) have demonstrated extraordinary capabilities across various natural language processing (NLP) tasks, including summarization, translation, question answering, and even basic logical reasoning. However, as the community pushes the boundary of what LLMs can achieve, a pressing question arises: Can LLMs perform deep relational reasoning?
Relational reasoning refers to the ability to analyze and manipulate structured relationships—such as those found in family trees, graph structures, or causal chains—to arrive at valid conclusions. Unlike surface-level understanding or pattern recognition, relational reasoning demands a multi-step logical process, integration of interdependent data points, and often an ability to simulate mental models of systems.
In this study, the authors conduct an empirical investigation into how well three state-of-the-art LLMs—DeepSeek-R1, DeepSeek-V3, and GPT-4o—perform on benchmark tasks designed specifically to test deep relational reasoning.
2. Motivation and Research Questions
Despite progress in Chain-of-Thought (CoT) reasoning, many LLMs fail when:
The relational structures grow complex.
The token length exceeds manageable limits.
The model’s output becomes partially complete or logically inconsistent.
The paper aims to answer the following questions:
To what extent can LLMs accurately solve deep relational problems involving multiple entities and rules?
How does reasoning quality degrade as task complexity increases?
What reasoning patterns and failure modes can we observe in models like DeepSeek-R1?
3. Benchmarks and Task Types
To answer these questions, the authors curated a diverse suite of reasoning tasks. These tasks are divided into two core categories:
3.1 Family Tree Reasoning Tasks
These simulate classic logic puzzles where one must infer familial relationships.
Example:
"If John is Sarah’s brother and Sarah is Emma’s mother, what is John’s relation to Emma?"
As problem depth increases, more intermediate inferences are required.
3.2 General Graph Reasoning Tasks
These tasks require models to analyze nodes and edges in an abstract graph.
Example:
"Node A is connected to Node B; Node B to Node C; is Node A indirectly connected to Node C?"
The authors vary graph size, edge types (directed/undirected), and reasoning path lengths.
4. Models Under Evaluation
4.1 DeepSeek-R1
Open-source, 70B parameters, reasoning-focused design.
Notable for long-form CoT capabilities and planning heuristics.
4.2 DeepSeek-V3
The flagship model of DeepSeek with MoE (Mixture-of-Experts) architecture.
Uses Multi-head Latent Attention and optimized for efficiency.
4.3 GPT-4o
The latest GPT model, optimized for multimodal input/output and long context windows.
Trained on extensive human-aligned data.
5. Methodology
5.1 Metrics
F1 Score: Combines precision and recall.
Exact Match Accuracy: Whether the final prediction is exactly correct.
Chain-of-Thought Trace Evaluation: Assess logical validity of intermediate steps.
5.2 Task Complexity Scaling
The benchmark includes 3 levels of task complexity:
Simple: 1–2 relations.
Moderate: 3–5 relations.
Complex: 6+ relations with distractors and ambiguous chains.
5.3 Prompt Design
Standardized zero-shot and few-shot prompts.
Chain-of-Thought enabled variants.
Model outputs were post-processed to extract logical graphs for validation.
6. Results and Performance Analysis
6.1 Overall Performance (F1 Score)
| Model | Simple Tasks | Moderate Tasks | Complex Tasks |
|---|---|---|---|
| DeepSeek-R1 | 96.2% | 88.7% | 75.3% |
| GPT-4o | 94.8% | 85.5% | 68.1% |
| DeepSeek-V3 | 91.3% | 81.0% | 62.6% |
DeepSeek-R1 consistently outperformed others across all difficulty levels. The performance gap widened in complex tasks requiring long-range reasoning.
7. Case Study: Family Tree Inference
Prompt Example:
John is the father of Sarah. Sarah is the sister of Mike. Mike is the father of Alice. What is the relation between John and Alice?
Correct Answer: Great-grandfather
DeepSeek-R1 Output:
Let's reason step-by-step.
John → father of Sarah.
Sarah ↔ sister of Mike ⇒ John is Mike’s father too.
Mike → father of Alice.
⇒ John is Alice’s grandfather.
✅ Correct reasoning path and final answer.
GPT-4o Output:
John is Sarah’s father. Sarah is Mike’s sister. Mike is the father of Alice.
⇒ John is Alice’s great-grandfather.
❌ Incorrect – skipped step inferring John's relation to Mike.
DeepSeek-V3 Output:
John → Sarah’s dad. Sarah ↔ Mike = siblings. Mike → Alice’s dad.
So John must be Alice’s grandfather.
✅ Correct answer, but missing verification step.
8. Observed Failure Modes
Despite DeepSeek-R1’s strong performance, all models shared some consistent failures:
🔻 8.1 Incomplete Chain of Thought
Some CoT outputs stop midway, especially for long prompts. This is often due to token length limits or premature termination.
🔻 8.2 Hallucinated Edges
In graph tasks, models sometimes inferred non-existent relationships, especially when distractor nodes are included.
🔻 8.3 Contradictory Conclusions
Rarely, a model’s intermediate steps were correct, but the final answer contradicted earlier reasoning.
9. DeepSeek-R1: Unique Patterns
A deeper look into DeepSeek-R1 reveals some interesting behaviors:
Planning Heuristics: Begins with outlining all known relationships before reasoning forward.
Verification Steps: Occasionally re-traces steps to double-check final conclusions.
Role-Based Deduction: Assigns implicit semantic roles to entities (e.g., "parent", "child") for generalized inference.
However, these strengths are undermined in extremely long or ambiguous problems, where output becomes disjointed or truncated.
10. Limitations of Current LLMs in Deep Reasoning
While DeepSeek-R1 shows impressive capabilities, the study emphasizes that no current LLM achieves human-level consistency in deep relational reasoning. Major limitations include:
Token Budget Constraints: Many reasoning chains exceed model’s context limits.
Lack of Persistent Memory: Models cannot store and recall intermediate facts as humans can.
Lack of External Knowledge Integration: Graphs aren’t updated in real-time unless prompted explicitly.
11. Future Directions
✅ Multimodal Reasoning
Combining visual graphs (e.g., family trees) with textual input could allow models to leverage both modalities.
✅ External Memory and Tools
Use of memory-augmented LLMs or tool-augmented approaches (e.g., calculators, theorem provers) may improve deductive reliability.
✅ Explainable Reasoning Evaluation
Develop formal evaluation methods for CoT traces that detect hallucination, contradiction, or logic flaws.
✅ Hierarchical Reasoning Decomposition
Models could break problems into nested sub-tasks using recursive CoT decomposition.
12. Theoretical Implications
The paper opens broader questions about the nature of reasoning in LLMs:
Do models truly reason, or simulate reasoning using memorized statistical patterns?
Can models generalize beyond seen patterns to novel relational structures?
Is emergent reasoning behavior the result of architecture, data, or training methods?
13. Code and Reproducibility
All experiments, benchmarks, and visualization scripts are available at:
🔗 https://github.com/kelvinhkcs/Deep-Relational-Reasoning
This ensures transparency and facilitates further research.
14. Conclusion
This comprehensive study provides compelling evidence that DeepSeek-R1 currently leads among open-access LLMs in tasks requiring deep relational reasoning. It demonstrates higher fidelity in CoT execution and greater accuracy across a range of structured inference problems.
However, as the complexity of tasks grows, all models degrade—highlighting the importance of model interpretability, memory augmentation, and improved training on structured logical inference.
The paper not only benchmarks the state-of-the-art but also issues a call to action for the AI community to design next-generation models that can reason with the depth, rigor, and abstraction that human cognition demands.
