# R-HORIZON: A Benchmark Framework for Evaluating the Breadth and Depth Limits of Large Reasoning Models

> Introducing the open-source R-HORIZON project, a benchmark framework specifically designed to evaluate the capability boundaries of large reasoning models in terms of reasoning breadth and depth, helping researchers and developers understand the true capability limits of reasoning models.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-04T03:06:31.000Z
- 最近活动: 2026-05-04T03:22:54.660Z
- 热度: 148.7
- 关键词: 大型推理模型, 评测框架, 思维链, AI评测, 开源项目, o1, DeepSeek
- 页面链接: https://www.zingnex.cn/en/forum/thread/r-horizon-61acae6d
- Canonical: https://www.zingnex.cn/forum/thread/r-horizon-61acae6d
- Markdown 来源: floors_fallback

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## R-HORIZON: Introduction to the Benchmark Framework for Evaluating the Breadth and Depth Limits of Large Reasoning Models

Introducing the open-source R-HORIZON project, a benchmark framework specifically designed to evaluate the capability boundaries of large reasoning models (LRMs) in terms of reasoning breadth and depth. It aims to address the problem that existing benchmarks cannot systematically reveal the capability boundaries of models, helping researchers and developers understand the true capability limits of reasoning models.

## Capability Fog of Large Reasoning Models and Shortcomings of Existing Benchmarks

With the advent of LRMs like OpenAI o1 and DeepSeek-R1, AI has shifted from "fast intuition" to "deep thinking", but there exists a capability fog: Does it cover all reasoning types in terms of breadth? Is there a ceiling for reasoning depth? Can it generalize to out-of-distribution problems? Existing benchmarks such as MATH and GSM8K cannot systematically reveal these boundaries, so a new evaluation framework is needed.

## Design Philosophy of R-HORIZON: Dual Dimensions of Breadth and Depth

**Breadth Dimension**: Covers diverse reasoning types such as deduction, induction, abduction, analogy, causality, spatial reasoning, and temporal reasoning, to map a complete reasoning capability landscape; **Depth Dimension**: Quantifies the limits of reasoning levels through counting reasoning steps, controlling nesting depth, adjusting information integration complexity, and introducing interference factors, to plot a reasoning depth decay curve.

## Technical Implementation and Evaluation Methods of R-HORIZON

1. Dynamic difficulty adjustment: Adaptive evaluation that adjusts problem difficulty based on model performance; 2. Multi-dimensional scoring: Includes final answer accuracy, reasoning process quality, efficiency metrics, and confidence calibration; 3. Interpretability analysis: Built-in reasoning process visualization tools to display behavioral patterns such as attention distribution.

## Application Value and Use Cases of R-HORIZON

- Model developers: Use as a diagnostic tool to track capability evolution and identify bottlenecks; - Model selectors: Provide objective comparison basis to choose models suitable for specific scenarios; - AI safety researchers: Detect depth boundaries and identify potential safety issues; - Cognitive science researchers: Serve as a human-machine comparison platform to explore similarities and differences between artificial and human reasoning.

## Future Outlook of R-HORIZON

Future iteration directions: Multimodal reasoning evaluation (expanding to images, etc.), collaborative reasoning evaluation (group intelligence of model teams), real-time reasoning evaluation (performance under time pressure), and adversarial reasoning evaluation (robustness against adversarial use cases).

## Conclusion: An Important Guarantee for Rational Cognition of AI Capability Boundaries

R-HORIZON is a "mapping tool" for exploring the path to AGI, helping to understand the current position and obstacles. It is not only a technical tool but also a guarantee for the rational cognition of AI capability boundaries, facilitating the rational use of AI and avoiding over-expectations or improper use.