Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
We introduce Knowledgeable Network of Thoughts (kNoT): a prompt scheme that advances the capabilities of large language models (LLMs) beyond existing paradigms like Chain-of-Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT). The key innovation of kNoT is the LLM Workflow Template (LWT), which allows for an executable plan to be specified by LLMs for LLMs. LWT allows these plans to be arbitrary networks, where single-step LLM operations are nodes, and edges correspond to message passing between these steps. Furthermore, LWT supports selection of individual elements through indexing, facilitating kNoT to produce intricate plans where each LLM operation can be limited to elementary operations, greatly enhancing reliability over extended task sequences. We demonstrate that kNoT significantly outperforms the state of the art on six use cases, while reducing the need for extensive prompt engineering. For instance, kNoT finds 92% accuracy for sorting 32 numbers over 12% and 31% for ToT and GoT, while utilizing up to 84.4% and 87.3% less task-specific prompts, respectively.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 27 references that are in the map. First posted on arXiv 21 Dec 2024.
Builds on
10 of 27 references.
Closest ancestors · 2
Same direction, newest first
- 2024Chain of Thoughtlessness? An Analysis of CoT in Planning8 May
- 2024How Likely Do LLMs with CoT Mimic Human Reasoning?25 Feb
Other references · 8
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
- 2019Analysing Mathematical Reasoning Abilities of Neural Modelscited by 331
- 2023REFINER: Reasoning Feedback on Intermediate Representationscited by 203
- 2024Transformers Can Do Arithmetic with the Right Embeddingscited by 77
- 2023A Survey of Reasoning with Foundation Modelscited by 70
- 2023GPT Can Solve Mathematical Problems Without a Calculatorcited by 68
- 2024Efficient Tool Use with Chain-of-Abstraction Reasoningcited by 33
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Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Models
- 2024Demystifying Chains, Trees, and Graphs of Thoughts
- 2026RouteGoT: Node-Adaptive Routing for Cost-Efficient Graph of Thoughts Reasoning
- 2025Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching
- 2023ThoughtSource: A central hub for large language model reasoning data
- 2025Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
- 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
- 2023Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models