Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2305.10601
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
aaea665f07c4037561c4606dd58387f4dbf4c721b0c78dbeee29bb7e44e736d0 - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Language agents / agent architectures
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to promp…
What This Teaches
How language models become agents through reasoning, acting, memory, tools, and interface design.
Related
Public Handling Notes
- EXTRACTED: Metadata and links are from public sources.
- INFERRED: Unclear-license full text, PDFs, and source code should not be mirrored into public wiki pages.
- AMBIGUOUS: Items discovered by keyword search are still separated by source identity and category heuristics.