Awesome-LLM-Reasoning
Compiles resources on chain-of-thought prompting to advanced reasoning systems like OpenAI o1 and DeepSeek-R1.
GraphCanon updated 3w · GitHub synced 3w
Decision brief
Awesome-LLM-Reasoning is designed for developers and researchers focused on advanced reasoning capabilities in language models using chain-of-thought prompting techniques and multimodal learning.
Good fit when
- Use when developing projects that integrate OpenAI's o1 or DeepSeek-R1 advanced reasoning systems as these resources are specifically referenced within the repository.
- Suitable for professionals interested in the latest advancements in prompt engineering tailored towards specific models like GPT-4o and o1 from the listed research papers.
Avoid when
- Avoid if your project does not require or involve advanced reasoning systems from specific providers such as OpenAI's o1, instead relying on general-purpose models.
- Not recommended for those working exclusively with non-language-model AI applications that do not focus on in-context learning or multimodal capabilities.
- Pricing:
- freemium - Freely available under the MIT license; resources linked within may have separate access costs, particularly proprietary models.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (99d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/atfortes/Awesome-LLM-ReasoningSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Covers chain-of-thought methods, prompt engineering, multimodal learning, in-context reasoning capabilities in language-models. Includes references to research papers and specific models from OpenAI GPT series and DeepSeek.
Capability facts
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README
Awesome LLM Reasoning
Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.
🗂️ Table of Contents
- Survey
- Analysis
- Technique
- 🔤 Reasoning in Large Language Models - An Emergent Ability
- 🧠 Multimodal Reasoning in Large Language Models
- 🤏 Scaling Smaller Language Models to Reason
- Other Useful Resources
- Other Awesome Lists
- Contributing
If you would like to test the symbolic reasoning ability of LLMs, take a look at: <a href=https://github.com/atfortes/LLMSymbolicReasoningBench>LLMSymbolicReasoningBench 😄
Survey
2025
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Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey. [code]
Yaoting Wang, Shengqiong Wu, Yuecheng Zhang, William Wang, Ziwei Liu, Jiebo Luo, Hao Fei. Preprint'25
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Recent Advances in Large Language Model Benchmarks Against Data Contamination: From Static to Dynamic Evaluation. [code]
Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray. Preprint'25
2024
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Attention Heads of Large Language Models: A Survey. [code]
Zifan Zheng, Yezhaohui Wang, Yuxin Huang, Shichao Song, Bo Tang, Feiyu Xiong, Zhiyu Li. Preprint'24
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Internal Consistency and Self-Feedback in Large Language Models: A Survey. [code]
Xun Liang, Shichao Song, Zifan Zheng, Hanyu Wang, Qingchen Yu, Xunkai Li, Rong-Hua Li, Feiyu Xiong, Zhiyu Li. Preprint'24
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Puzzle Solving using Reasoning of Large Language Models: A Survey. [code]
Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou. Preprint'24
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Large Language Models for Mathematical Reasoning: Progresses and Challenges.
Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, Wenpeng Yin. ACL'24
2022
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Towards Reasoning in Large Language Models: A Survey. [code]
Jie Huang, Kevin Chen-Chuan Chang. ACL'23 Findings
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**[Reasoning with Language Model Promp
For agents
This page has a .md twin and JSON over the API.